Sensing system with features for determining physiological metrics of a subject and for predicting electrophysiological events of a subject

ABSTRACT

Some systems, devices and methods detailed herein provide a system for use in determining metrics of a subject. The system can provide, as an output, a function-metric value determined based on a defined relationship between physiological measures and a chronological age.

RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.63/333,832, filed on Apr. 22, 2022 and U.S. Provisional Application No.63/402,756, filed on Aug. 31, 2022. The entire contents of which arehereby incorporated by reference.

TECHNICAL FIELD

This document describes systems and computer-implemented methods forproviding improved determination of a physiological metrics of asubject.

BACKGROUND

Neurodegenerative disorders and diseases can be difficult to detect forearly intervention, and the early signs of neurodegeneration can gounnoticed until intervention may be too late. Early detection ofaccelerated decline in brain anatomy and function can lead to earlyintervention, which is the time when interventions are most effective.Neurodegenerative disorders such as Alzheimer's, Huntington's,Parkinson's, and others can occur as people get older. Neurodegenerationcan occur at various different chronological ages for different peoplebased on a variety of factors such as such as demographics (e.g.,gender, race, ethnicity or family histories), health conditions, healthdiagnoses, current or prior behaviors (e.g., smoking, alcoholconsumption, exercise, diet, among others), historical behaviors, andenvironmental factors.

While a chronological age can be easily determined, a person's brain agemay differ from their chronological age. In some instances,neurodegeneration can cause a person's brain age to be greater (i.e.,older) than their chronological age. Accordingly, brain age has beenused as a biomarker of aging, due to the effects of age on thestructural and functional properties of the human brain.

Current solutions to determine a person's brain age use structural andfunctional MRI scans or laboratory-based polysomnographic (PSG)recordings, which are costly, inappropriate as a large-scale screeningtool, and inappropriate for continuous monitoring over multiple nights.

The four phases of sleep include a stage referred to as deep sleep, slowwave sleep (SWS), or N3 sleep. Each of the four phases of sleep havephysiologic characteristics and benefits that can differ between thephases. Deep sleep has a role in declarative memory consolidation, aswell as a restorative role associated with energy restoration, immunity,hormone regulation, and cleaning of metabolites.

Closed-loop auditory stimulation of sleep slow oscillations (SOs) is anapproach to deep sleep enhancement. However, sensory (e.g., auditory)stimulation during sleep is often applied at times that do not correctlycorrespond to the aimed sleeping patterns of a subject, such as thepositive SO peak. Additionally, bioelectrical signals (e.g.,electroencephalogram or EEG signals) can include signal noise that ischallenging to filter in real time because filtering introduces shapeand phase distortions in bioelectrical signals (e.g., EEG).

SUMMARY

This document describes techniques, methods, and systems for determiningphysiological metrics of a subject and for real-time prediction ofevents of a subject's brain function. Some embodiments of systems andmethods detailed herein include providing improved device and methodthat extract and analyze biological features during sleep from EEG andother biosensors, and user characteristics (e.g., gender, race,ethnicity or family histories), health conditions, health diagnoses,current behaviors, and historical behaviors (e.g., use of alcohol,drugs, exercise, etc.) and environmental factors. The biologicalfeatures and user characteristics can be analyzed and compared tobiological features and user characteristics of the same by age group,by the same gender, or by other groups or factors to calculatephysiological metrics of the subject and provide a level of confidencefor the calculated metrics. For example, the other groups can includegroups based on activity level, geographic location, education level,professional level, lifestyle groups, pre-menopausal and post-menopausalgroups, menstrual cycle groups, among others. In some aspects, thephysiological metrics can be a biological brain age of the subject.

In some embodiments, a system for use in determining metrics of asubject is provided. The system includes one or more processors and amemory storing instructions that, when executed by the processors, causethe processors to perform operations. The operations may include:receiving physiological measures of the subject recorded at least partlywhile the subject is asleep. The instructions also include receivingdemographic data for the subject, the demographic data may include achronological age for the subject when the physiological measures wererecorded. The instructions also include generating, using thephysiological measures and from the demographic data, segmentedtraining-data that specifies a plurality of epochs of time and data forthe subject in each epoch. The instructions also include generating,using the segmented training-data, sleep-structure features for thesubject. The instructions also include selecting a subset of thesleep-structure features as selected features. The instructions alsoinclude generating, using the selected features, one or morefunction-metric classifiers may include training a model that defines atleast one relationship between the physiological measures and thechronological age, the function-metric classifier configured to:receive, as input, new physiological measures. The instructions alsoinclude provide, as output, a function-metric value determined based onthe defined relationship between the physiological measures and thechronological age.

Implementations may include one or more of the following features. Thedemographic data includes at least one of the group consisting of anidentifier, a gender, sociodemographic data, medical data, behavioraldata, and lifestyle data that have been entered by the subject into aninput device of the system. The physiological measures of the subjectmay include at least one of the group consisting of a frontalelectroencephalography (EEG) channel, two frontal EEG channels, foreheadphotoplethysmography (PPG), blood oxygen saturation (SPO₂),electromyography (EMG), electrooculography (EOG), electrodermal activity(EDA), and actigraphy data. The physiological measures of the subjectcan be recorded at least partly while the subject is asleep and providedwith at least one stimuli of the group consisting of audio stimuli,light stimuli, vibratory, electrical stimuli, open-loop stimuli, andclosed-loop stimuli; and the physiological measures may include timinginfo defining timing of stimuli provided to the subject.

Implementations may include one or more of the following features.Generating, using the physiological measures and from the demographicdata, segmented training-data may include: applying band-pass filters toat least some of the physiological measures; combining at least twofrontal/prefrontal EEG signals to create a virtual frontal/prefrontalEEG signal; detecting artifacts in the acquired signals, e.g. movementartifacts; extracting heartbeat times from PPG signals by detectingpeaks in the PPG signals; segmenting the physiological measures into aplurality of epochs of time; generating, for each epoch of time, atleast one of the group may include of multiple time-domain features,frequency domain features, and nonlinear or complex signal descriptives;accessing tagging data that tags each epoch of time with a sleep stagefrom the group may include of wakefulness, rapid eye movement (rem)sleep, non-rem sleep stage 1 (n1), non-rem sleep stage 2 (n2), non-remsleep stage 3 (n3), and non-rem sleep stage 4 (n4). Generating, usingthe segmented training-data, sleep-structure features for the subjectmay include: determining macrostructure features for the subjectdescribing at least one of the group may include of sleep-stageduration, sleep-stage percentage, sleep-stage transition probability,sleep fragmentation, and awakenings; determining microstructure featuresfor the subject describing at least one of the group may include ofstage-specific EEG features, waveform-specific EEG features, andstimulus-response EEG features; determining cardiac features for thesubject using at least one of the group may include of heartbeat timesand tagging data that tags epochs of time with sleep stage; anddetermining respiratory features for the subject describing at least oneof the group may include of blood oxygenation, heart rate, heart beattimes, and sleep apnea, using at least one of the group may include ofblood oxygen saturation (SPO₂) and the tagging data.

Implementations may include one or more of the following features.Selecting a subset of the sleep-structure features as selected featuresmay include: transforming at least one of a plurality of featuresselected from the group may include of macrostructure features,microstructure features, cardiac features, and respiratory features; andaggregating sleep-structure features from multiple sleep-sessions.Selecting a subset of the sleep-structure features as selected featuresmay include sequentially calculating the cross-validated mean absoluteerror (MAE) with a regression model, where the regression model is anextreme learning machine (ELM) regressor, a least absolute shrinkage andselection operator (LASSO), or a regression model trained under theGradient Boosting framework (e.g. gradient boosted trees).

Implementations may include one or more of the following features.Training the model that defines at least one relationship between thephysiological measures and the chronological age may include determininghyperparmeters for the model using a Bayesian optimization algorithmtargeting a repeated k-fold cross-validation using at least one of thegroup may include of a regression problem; a regression with a lossfunction based on a residual-label covariance analysis; a deep labeldistribution learning algorithm based on neural networks; a cascade of amulti-class classification model (e.g. a support vector machine)followed by several regression models where each is trained for aspecific demographic or physiological class (group). Training a modelthat defines at least one relationship between the physiologicalmeasures and the chronological age may include refining the model toreduce age-dependent bias. The classifier is further configured toprovide, as output, at least one of the group may include of aconfidence value, a variance-from-chronological-age value, a modelinterpretation, an interpretation of the model's output, ahuman-readable instruction displayable to a user of an output device,and an automation-instruction that, when executed by an automated devicecauses the automated device to actuate. The operations further mayinclude distributing the function-metric classifiers to a plurality ofuser devices that are configured to sense new physiological measures ofother subjects at least partly while the other subjects are asleep. Theoperations further may include: receiving new physiological measures ofthe subject recorded at least partly while the subject is asleep andrecorded after the function-metric classifiers have already beengenerated; submitting the new physiological measures to at least one ofthe function-metric classifiers as the input; and receiving as outputfrom the at least one function-metric classifier the function-metricvalue.

In an example embodiment, a method for providing stimulation to asubject is provided. The method can include receiving a data-stream forthe subject, the data-stream comprising a real-time EEG signal generatedby one or more EEG sensors gathering data of ongoing brain activity ofthe subject; identifying in the data-stream a record of a current slowoscillation (SO) that contains data of an incomplete SO of the ongoingbrain activity; extracting one or more SO features for the current SOfrom the record of the current SO; determining, from the SO features,one or more predicted SO values, the predicted SO values each being aprediction of a future event at which the current SO will exhibit atarget morphology.

Implementations may include one or more of the following features. Themethod can include engaging a stimulation device to provide the subjectwith a stimulation signal based on the predicted SO values such that thesubject receives the stimulation signal while the brain activity of thesubject is generating the current SO. The method can include determininga delay interval based on the predicted SO values; delaying for thedelay interval; and sending an activation command to the stimulationdevice upon expiration of the delay interval. The method can includeengaging the stimulation device comprises determining that the currentSO is a typical SO. The method where the one or more SO features thatare extracted are selected from the group (SO group) may include of i) apositive-to-negative zero-crossing (zx1), ii) a negative-to-positivezero-crossing (zx2), iii) a point after zx1 at which a slope of thedata-stream falls under a negative threshold (steady1), iv) SO negativepeak timing (neg_time), v) a point before zx2 at which the data-streamfalls under a defined positive threshold (steady), vi) a SO positivepeak timing (pos_time), and vii) a points at which data-stream valueexceeds a defined percentage of the SO negative peak amplitude(neg_percent). The one or more predicted SO values are also selectedfrom the SO group. The one or more SO values are different than the SOgroup. The method can include determining, from the SO features, one ormore predicted SO values may include submitting, to a so-classifier, theSO features and receiving the predicted SO values. The so-classifier iscreated via training on a dataset of training-so features and matchingtraining-so values. The dataset is constructed to exclude atypicaltraining-so features. The classifier is retrained using the SO featuresof a single night's sleep during the single night's sleep. Theclassifier is trained for a specific morphological type of SO. Theclassifier is trained for the subject using training data from thesubject. The classifier is trained in real-time using the data from acurrent sleep session. The method can include determining of one or morepredicted SO values is responsive to determining that the subject is ina particular sleep stage Implementations of the described techniques mayinclude hardware, a method or process, or computer software on acomputer-accessible medium.

In an example embodiment, a system for providing stimulation to asubject is provided. The system can include a data acquisition devicethat can include a body, one or more EEG sensors, and at least onestimuli generator. The system also includes one or more processors andmemory storing instructions that, when executed by the processors, causethe processors to perform operations. The operations may include:receiving a data-stream for the subject, the data-stream may include areal-time EEG signal generated by the one or more EEG sensors gatheringdata of ongoing brain activity of the subject; identifying in thedata-stream a record of a current slow oscillation (SO) that containsdata of an incomplete SO of the ongoing brain activity; extracting oneor more SO features for the current SO from the record of the currentSO; and determining, from the SO features, one or more predicted SO thepredicted SO timings each being a prediction of a future event (e.g.,time, amplitude, category) at which the current SO will exhibit a targetmorphology.

Implementations may include one or more of the following features. Thesystem where the body is a headband that includes a curved shape that isconfigured to extend around each ear of a subject and under a nape ofthe back of a subject's head. The operations may include: engaging theat least one stimuli generator to provide the subject with a stimulationsignal at the predicted SO values such that the subject receives thestimulation signal while the brain activity of the subject is generatingthe current SO. The stimuli generator generates audio stimuli. Theoperations may include: determining, from the SO features, one or morepredicted SO values may include submitting, to a so-classifier, the SOfeatures and receiving the predicted SO values. The one or more SOfeatures that are extracted are selected from the group (SO group) mayinclude of i) a positive-to-negative zero-crossing (zx1), ii) anegative-to-positive zero-crossing (zx2), iii) a point after zx1 atwhich a slope of the data-stream falls under a negative threshold(steady1), iv) SO negative peak timing (neg_time), v) a point before zx2at which the data-stream falls under a defined positive threshold(steady), vi) a SO positive peak timing (pos_time), and vii) a points atwhich data-stream value exceeds a defined percentage of the SO negativepeak amplitude (neg_percent). The method where engaging the stimulationdevice may include: determining a delay interval based on the predictedSO values; delaying for the delay interval; and sending an activationcommand to the stimulation device upon expiration of the delay interval.Implementations of the described techniques may include hardware, amethod or process, or computer software on a computer-accessible medium.

Implementations may include one or more of the following features. Themethod where engaging the stimulation device may include: determining adelay interval based on the predicted SO values; delaying for the delayinterval; and sending an activation command to the stimulation deviceupon expiration of the delay interval. Implementations of the describedtechniques may include hardware, a method or process, or computersoftware on a computer-accessible medium.

Particular implementations can, in certain instances, realize one ormore of the following advantages. For example, the described systems andmethods can advantageously provide users with the ability to assesstheir functional metrics in a single night's sleep or over multiplenights' sleep. This can provide the user a short-term result based on asingle night's sleep and a longer term result based on multiple nights'sleep data that can be compared to identify trends. By providing aresult based on multiple nights' sleep, the described systems andmethods can advantageously mitigate the issue of night-to-nightvariability in the used biological sleep features. The users can also beprovided with assessments of their physiological metrics that areconsistent with early signs of cognitive decline. Additionally, thedetermined brain age from the described systems and methods can beobtained and provided to the subject when the subject is in their normalsleep environment (e.g., at home). This provides data to the user whenthey are not disturbed by anxiety, not in an unfamiliar environment oruncomfortable bed, or other factors that would impact their ability tosleep normally. The described systems and methods do not involve atechnician installing multiple uncomfortable leads on the subject, whichcan be advantageous for the subject because the system is muchaccessible as well as being more time and cost effective than asleep-study, MRI, or other brain function tests that take place in aclinical environment. The system can also be used to monitoreffectiveness of clinical or wellness interventions. For example, thesystem can assess if a medication, drug, or other intervention slowedthe progression of brain age for the subject. The system can assess iftaking up meditation, improving sleep hygiene, exercising, or joiningpeer groups have an impact on the progression of brain age.

Because SO-spindle coupling is believed to be impaired during traumaticbrain injury (TBI), this technology can be used to aid in diagnosing ormeasuring severity of TBI. Limited diagnostic methods are previouslyavailable to detect and characterize TBI. CT and MRI scans can detectbleeding from TBI but struggle with detection of non-structural damage.The symptoms of mild TBI (mTBI) lack specificity making diagnosischallenging. However, accurate diagnosis is important to preventrepetitive injury and the risk of “second impact syndrome” in which asubject suffers a second impact injury before the previous injury isfully healed. In order to facilitate accurate diagnosis, an objective,sensitive, non-invasive way to assess TBI is required, which can beprovided by this technology. Memory consolidation is believed to bedependent upon the synchronization of the hippocampus, thalamus andmedial prefrontal cortex (mPFC) bidirectional communication during SlowWave Sleep (SWS). TBI is believed to impure this synchronization.Therefore, this technology can provide a functional assessment of TBIand identify lingering impacts, and users (e.g., clinicians, patients,researchers) can trust this assessment as objective, sensitive, aproduct of a non-invasive process.

For example, the described systems and methods can advantageouslyprovide precisely timed stimulation events based on a robust estimationof the filter distortion based on the currently detected event inreal-time. Additionally, the described systems and methodsadvantageously provide a functional relationship between the morphologyof the currently detected event and the expected filter distortion. Thesystems and methods described herein advantageously provide awithin-event approach to stimulation timing (e.g., a within-SOapproach), such that the prediction of an outcome (e.g., the timing ofthe SO positive peak) within an ongoing event is tailored based on thecharacteristics of the ongoing event, and not only limited to previousevents. Additionally, the described systems and methods canadvantageously provide the decision on whether or not to apply thesensory stimulation based on the morphology of the currently detectedevent in real-time.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features,objects, and advantages will be apparent from the description anddrawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 shows an example system for determining a functional metric of asubject, consistent with embodiments of this disclosure.

FIG. 2 shows an example data acquisition device on the head of asubject.

FIG. 3 shows an example system for determining metrics of a subject.

FIG. 4 shows an example process that can be used to produce classifiersable to evaluate subject data and generate function metrics.

FIG. 5 shows an example process that can be performed by the system.

FIG. 6 shows an example process for determining metrics of a subject.

FIG. 7 is a block diagram of computing devices that may be used toimplement the systems and methods described in this document, as eithera client or as a server or plurality of servers.

FIG. 8 is an example user interface showing a subject's brain age andestimated future brain age.

FIG. 9 shows an example system for determining a functional metric of asubject, consistent with embodiments of this disclosure.

FIG. 10A shows an example data acquisition device on the head of asubject.

FIG. 10B shows the data acquisition device of FIG. 2A on the head of thesubject.

FIG. 10C shows the data acquisition device of FIGS. 2A and 2B removedfrom the head of the subject.

FIG. 10D shows the data acquisition device of FIGS. 2A and 2B removedfrom the head of the subject.

FIGS. 11A-11C show an example system and data for determining timing ofelectrophysiological events of a subject.

FIG. 12 shows an example process that can be used to produce classifiersable to evaluate subject data and generate stimulus to a subject basedon determined timings of electrophysiological events of the subject.

FIG. 13 shows an example process that can be performed by the system.

FIG. 14 shows an example process for generating stimulus to a subjectbased on determined timings of electrophysiological events of thesubject.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

This document describes techniques, methods, and systems for determiningphysiological metrics of a subject. Some embodiments of systems andmethods detailed herein include providing improved device and methodthat extract and analyze biological features during sleep from EEG andother biosensors, and user characteristics (e.g., gender, race,ethnicity or family histories), health conditions, health diagnoses,current behaviors, and historical behaviors (e.g., use of alcohol,drugs, exercise, environmental factors, etc.). The biological featuresand user characteristics can be analyzed and compared to biologicalfeatures and user characteristics of the same by age group to calculatephysiological metrics of the subject and provide a level of confidencefor the calculated metrics. In some aspects, the physiological metricscan be a biological brain age of the subject.

This document describes techniques, methods, and systems for determiningthe timing of a subject's electrophysiological events with sufficientspeed and precision that the predictions can be used for the timing of,for example, stimulus to the subject via a worn medical or wellnessdevice as the subject sleeps. For example, some types of auditory,electrostimulative, or tactile stimulation can call for timing precisionin order to be delivered during particular points in a slow waveoscillation (SO) of brain activity (e.g., coordinated activity of largepopulations of neurons consisting of an alternation of active periods ofUp states and silent periods of Down states), and this technology can beused to predict, based on sensing of early portions of the SO, timing ofevents later in the same SO. This can allow for accurate and fastreal-time sensing, predicting, and stimulating, in a way that is notpossible with, for example, post-sensing processing of SO data doneafter a sleep session, or from data from prior SOs in the same sleepsession, or other sensing sessions.

Referring to the figures, FIG. 1 illustrates an example of a system 100for determining physiological metrics of a subject. The system 100 caninclude a data acquisition device 101 that has one or more physiologicalsensors 104 and one or more stimuli generators 105. The system 100 caninclude a user interface 106, training computer hardware 110, operatingcomputing hardware 116, and a data source 118. The system 100 can beconfigured to collect data from one or more subjects 108, as will bedescribed in further detail below.

In some aspects, the data acquisition device 101 can be worn by thesubject 108 to collect data from the one or more physiological sensors104. For example, the data acquisition device 101 can be configured todetect, measure, monitor, and record brain activity usingelectroencephalography (EEG), eye activity using electrooculography(EOG), muscle activity using electromyography (EMG), cardiac activityusing electrocardiography (ECG), respiration rate (e.g., usingrespiratory inductance plethysmography (RIP), pressure sensor, and/or atemperature sensor), oxygen saturation (e.g., using pulse oximetry),heart rate (HR), blood flow, actigraphy during sleep, or any combinationthereof. The stimuli generators 105 can generate audio stimuli, opticalstimuli, visual stimuli, tactile stimuli, or combinations thereof to thesubject 108, and the physiological sensors 104 can collect data thatreflects the subject's 108 response to the stimuli.

The data collected by the data acquisition device 101 can becommunicated throughout the system 100. For example, the data from thedata acquisition device 101 can be displayed at the user interface 106,sent to the training computing hardware 110, sent to the operatingcomputing hardware 116, and sent to the data source 118. Each of thedata acquisition device 101, the user interface 106, the trainingcomputing hardware 110, and the operating computing hardware 116 canperform one or more of the processing steps described in further detailbelow (see e.g., FIGS. 4-6 ).

Referring now to FIG. 2 , an example of a data acquisition system 200that includes a data acquisition device 201 is shown. In some aspects,the data acquisition device 201 can be the data acquisition device 101of FIG. 1 . The data acquisition device 201 can be a head-worn sensingdevice that includes one or more sensors. The data acquisition device201 can have a body 214 that can be a breathable material, for example amesh material. The breathable material can allow the skin beneath thebody 214 to breathe. The breathable material can be elastic and/orinelastic. The elastic properties of the body 214 can be configured toinhibit or prevent the data acquisition device 201 from slipping duringuse, such as when the user moves during sleep (e.g., when the usershifts position or when one of their limbs or another person contactsthe data acquisition device 201). The body can extend partially orcompletely around a perimeter of a head 208 of a subject.

The data acquisition device 201 can be a removably attachable headband,cap, hat, strip (e.g., adhesive or hook-and-loop style fastening strip),biased band, or any combination thereof. The data acquisition device 201can have the shape of a closed or open loop (e.g., annular orsemi-annular shape). The data acquisition device 201 can extendpartially or completely around a perimeter of the head 208.

The data acquisition device 201 can have a body 214 comprising multiplebands 215, for example, a first band 215 a, a second band 215 b, and athird band 215 c. The first, second, and third bands 215 a, 215 b, 215 ccan be separate bands and/or can be different band portions of a singleunitary band. For example, the first, second, and/or third bands 215 a,215 b, 215 c can be attached to or integrated with one another atattachment region 215 d. The data acquisition device 201 can form aheadband. The first band/band portion 215 a can form a front band/strap.The second and third bands/band portions 215 b, 215 c can form backbands/straps. The data acquisition device 201 can have a ‘split band’ inthe back of the head 208 formed by the second and third straps 215 b,215 c, where the bottom band (e.g., the third band 215 c) can beconfigured to cup under the curve of the back of the head to reduce anypotential slippage/movement of the headband.

A band adjuster 205 a can enable back straps (e.g., bands 215 b and 215c) to adjust to contour to person's head. The band adjuster 205 a canallow the second band 215 b to be adjusted independently from the thirdband 215 c. The band adjuster 205 a can allow the third band 215 c to beadjusted independently from the second band 215 b. The band adjuster 205a can allow the angle between the second and third bands 215 b, 215 c tobe adjusted. Alternatively or additionally, the data acquisition device201 can have another band adjuster 205 b that can have the samefunctionality as the band adjuster 205 a. The data acquisition device201 can have one or more length adjustment mechanisms configured toallow the length of the one or more bands to be increased, decreased,and/or locked into position. For example, the data acquisition device201 can have a first length adjustment mechanism 204 a for the firstband/band portion 215 a, a second length adjustment mechanism 204 b forthe second band/band portion 215 b, a third length adjustment mechanism204 c for the third band/band portion 215 c, or any combination thereof.

The elastic body and slip resistant edges can be configured to keep oneor more sensors of the data acquisition device 201 in position duringuse such that there is strong contact and less resistance to movement atthe point where the sensors come into contact with the skin. This canadvantageously ensure that the device sensors can have reliable contactwith the skin.

Alternatively or additionally, the data acquisition device 201 can haveone or multiple expandable mechanisms 204 a, 204 b, 204 c configured tokeep the sensors of the data acquisition device 201 in position duringuse such that there is strong contact and less resistance to movement atthe point where the sensors come into contact with the skin. Theexpandable mechanism can allow the sensors to contact the skin withprecise pointed pressure (e.g., from pressure provided by the expandablemechanism). The expandable mechanism can be behind one or more sensorsof the data acquisition device 201, for example, behind all of thesensors of the data acquisition device, or behind any lesser number ofsensors of the data acquisition device 201. The expandable mechanism canbe an inflatable bladder. The expandable mechanism (e.g., the inflatablebladder) can be configured to expand to press one or more sensors intothe skin. The expandable mechanism can remain expanded during use.

The expandable mechanism can be expanded from an unexpandedconfiguration to an expanded configuration. The unexpanded configurationcan have a first volume and the expanded configuration can have a secondvolume larger than the first volume. The first volume can be zero orgreater than zero. The second volume can be, for example, about 1 mL toabout 50 mL, including every 1 mL increment within this range. Theexpandable mechanism can be expanded until a predetermined pressurethreshold is detected between the skin and one or more of the devicesensors, for example, by one or more pressure sensors associated withthe expandable mechanism. The expandable mechanism (e.g., inflatablebladder) can advantageously enable the data acquisition device 201 tocreate skin-sensor contacts that have known and reproducible skin-sensorcontact pressures or other measurable quantity that can characterize thecontact between the sensors and the skin, or that otherwise fall withinan acceptable tolerance such that the device can accurately andprecisely record various physiological activity of the subject (e.g.,brain activity).

The data acquisition device 201 can be configured to measure and collectone or more physiological parameters during sleep. For example, the dataacquisition device 201 can be configured to detect, measure, monitor,and record brain activity, eye activity, muscle activity (e.g., bodyposture, limb movements), cardiac activity (e.g., heart rate, heart ratevariability (HRV)), respiration activity (e.g., respiration rate), bloodoxygen saturation, blood flow rates, or any combination thereof. Forexample, the data acquisition device 201 can be configured to detect,measure, monitor, and record brain activity using electroencephalography(EEG), eye activity using electrooculography (EOG), muscle activityusing electromyography (EMG), cardiac activity using electrocardiography(ECG), respiration rate (e.g., using respiratory inductanceplethysmography (RIP), pressure sensor, and/or a temperature sensor),oxygen saturation (e.g., using pulse oximetry), heart rate (HR), bloodflow, actigraphy during sleep, or any combination thereof. The dataacquisition device 201 can be configured to detect, measure, monitor,and record pressure and temperature, for example, using one or morepressure sensors and/or one or more temperature sensors. The dataacquisition device 201 can perform polysomnography (PSG) tests and cancollect polysomnographic data. The data that is collected is referred tothroughout as acquired data, raw data, and/or sleep data.

The data acquisition device 201 can have one or more data acquisitionmodules 218 (also referred to as electronics modules 218), for example,1 to 5 data acquisition modules 218, including every 1 module incrementwithin this range (e.g., 2 electronics modules). For example, the dataacquisition device 201 can have one electronics module 218. In anotherexample, the data acquisition device 201 can have a plurality of dataacquisition modules 218 spaced apart around the data acquisition device201 to provide sensors at a variety of positions around the head 208 ofthe subject to optimize data collection.

The one or more data acquisition modules 218 can be configured tomonitor and record one or more physiological activities during sleep.For example, the data acquisition modules 218 can be configured todetect, measure, monitor, and record brain activity, eye activity,muscle activity, cardiac activity, respiration activity, blood oxygensaturation, blood flow, actigraphy, or any combination thereof (e.g.,using EEG, EOG, EMG, ECG, RIP, pulse oximetry, or any combinationthereof, respectively). The one or more data acquisition modules 218 canbe computer interfaces, for example, brain computer interfaces (BCIs).

The data acquisition modules 218 can have one or more electrodes,sensors (e.g., biosensors), accelerometers, or any combination thereof.For example, the data acquisition modules 218 can have one or more EEGbiosensors, EOG biosensors, EMG biosensors, ECG biosensors, respirationrate biosensors, pulse oximetry biosensors, HRV biosensors, temperaturesensors, pressure sensors, or any combination thereof, including one ormore reference sensors and/or one or more ground electrodes.

The data acquisition modules 218 can have a single-channel and/or amulti-channel EEG system. The multi-channel EEG system can be operatedas a single channel EEG system. The EEG system (single or multi-channel)can include one or more EEG sensors. The data acquisition device 201(e.g., the data acquisition modules 218) can have 1 to 10 EEG sensors,including every 1 EEG sensor within this range (e.g., 4 EEG electrodes).The data acquisition modules 218 can have more than 10 sensors (e.g., 1to 100 EEG sensors). The data acquisition modules 218 can have an EEGsensor array or an EEG sensor network (e.g., of 2 to 10 or moresensors). One of the EEG sensors can be a ground electrode. The EEGsystem can have one or multiple reference electrodes (e.g., one or tworeference electrodes). The electronics module 218 can have, for example,three channels of frontal EEG and one EEG reference sensor or threechannels of prefrontal EEG and one EEG reference sensor. The EEGelectrodes can be positioned on the forehead, for example, the EEGelectrodes can be placed at forehead positions such as Fp1 and Fp2. TheEEG electrodes can be placed according to the international 10-20system.

The data acquisition modules 218 can have 2, 3, or 4 EOG sensors. TwoEOG sensors can detect/measure movement of one or both eyes. Forexample, two EOG sensors can be positioned to detect/measure eyemovement of the left and right eyes (e.g., a first EOG sensor can bepositioned on the right outer edge of the right eye and a second EOGsensor can be positioned on the left outer edge of the left eye), twoEOG sensors can be positioned to detect/measure eye movement of only theleft eye (e.g., a first EOG sensor can be positioned on the right outeredge and a second EOG sensor can be positioned on the left outer edge ofthe left eye), or two EOG sensors can be positioned to detect/measureeye movement of only the right eye (e.g., a first EOG sensor can bepositioned on the right outer edge and a second EOG sensor can bepositioned on the left outer edge of the right eye). Three EOG sensorscan be positioned to detect/measure eye movement of the left and righteyes (e.g., a first EOG sensor can be positioned on the right outer edgeof the right eye, a second EOG sensor can be positioned on the leftouter edge of the left eye, and a third EOG sensor can be positionedbetween the left and right eyes). The three EOG sensors can selectivelydetect/measure eye movement of the left and/or right eyes, with thefirst and third EOG sensors configured to detect/measure movement of theright eye, with the second and third EOG sensors configured todetect/measure movement of the left eye, and with the first and secondEOG sensors configured to detect/measure movement of the left and righteyes together. Four EOG sensors can be positioned to detect/measure eyemovement of the left and right eyes (e.g., first and second EOG sensorscan be positioned on first and second sides of the left eye and thirdand fourth EOG sensors can be positioned on first and second sides ofthe right eye). The “outer edges” of the eyes can be in line with theeyes, above the eyes and/or below the eyes.

The data acquisition system 200 can have 1 to 6 EMG sensors, includingevery 1 EMG electrode increment within this range (e.g., 2 EMGelectrodes).

The data acquisition system 200 (e.g., the data acquisition device 201and/or the data acquisition modules 218) can have 1 to 10 ECG sensors,including every 1 ECG electrode increment within this range (e.g., 1, 2,or 3 ECG electrodes). The ECG sensors can be used to measure HRV. TheECG sensors can be used to determine HRV.

The data acquisition system 200 (e.g., the data acquisition device 201and/or the data acquisition modules 218) can have 1 to 10 heart ratesensors, including every 1 heart rate sensor increment within this range(e.g., 1, 2, or 3 heart rate sensors). The heart rate sensors can beused to measure HRV. The heart rate sensors can be used to determineHRV.

The data acquisition system 200 (e.g., the data acquisition device 201and/or the data acquisition modules 218) can have one or multiplepressure sensors (e.g., pressure transducers) and/or temperature sensors(e.g., thermocouples) configured to monitor respiration. For example,the data acquisition device 201 can have 1 to 4 pressure sensors,including every 1 pressure sensor increment within this range (e.g., 1or 2 pressure sensors). The data acquisition device 201 can have 1 to 4temperature sensors, including every 1 temperature sensor incrementwithin this range (e.g., 1 or 2 temperature sensors). The pressureand/or temperature sensors can be positionable near the nostrils and canbe configured to detect the pressure/temperature changes that occur whena user inhales and exhales. The pressure and/or temperature sensors canbe attached to or integrated with the data acquisition device 201 suchthat when the data acquisition device 201 is removably secured to ahead, the pressure and/or temperature sensors are positioned in abreathing flow path (e.g., near the nostrils and/or mouth, for example,for mouth breathers).

The data acquisition device 201 can have a pulse oximetry sensor thatcan be removably attachable to an ear, for example, to an ear lobe. Thedata acquisition system 200 can have a pulse oximetry sensor that can beremovably attachable to a finger. The finger pulse oximetry sensor canbe in wired or wireless communication with the data acquisition device201 (e.g., to the electronics module 218) and/or to the data displaydevice. The ear pulse oximetry sensor can be attached to or integratedwith the data acquisition device 201. The pulse oximetry sensor (ear andfinger sensor) can be a component of a clip. The clip can attach to(e.g., clip to) an ear lobe or a finger. The clip can be attached to orintegrated with the data acquisition device 201, for example, to thebody 214. The pulse oximetry sensor can be placed on the forehead. Theforehead pulse oximetry can be attached to or integrated in the dataacquisition device 201.

The data acquisition device 201 can have one or more pressure sensors(e.g., 1, 2, 3, 4, 5, 6 or more) configured to detect when the dataacquisition device 201 is attached to a head, for example, by measuringthe amount of force exerted against each of the pressure sensors. Thedata acquisition system 200 can be configured to detect whether the dataacquisition device 201 is properly positioned on the head, for example,by detecting and/or comparing the different pressures measured by theone or more pressure sensors (e.g., by calculating one or more pressuredifferentials). The pressure sensors can also be used to determinewhether the position can be improved or further optimized, for example,for more accurate and/or reliable data collection. The data acquisitiondevice 201 can be activated (e.g., automatically or manually) whenpositioned on the head 208 as a result of one or more pressure sensorsexceeding a pressure threshold. The data acquisition device 201 can beactivated (e.g., automatically or manually) when positioned on the head208 as a result of one or more differential pressure differentials(e.g., between two sensors) falling below a differential pressurethreshold.

For example, a first pressure sensor can be on a first side of the dataacquisition device 201 and a second pressure sensor can be on a secondside of the data acquisition device 201. The pressure sensors can beseparated by about 1 degree to about 180 degrees as measured from acenter of the data acquisition device 201 (e.g., along a longitudinaland/or transverse axis), including every 1 degree increment within thisrange. The center of the data acquisition device 201 can fall betweentwo inner sides of the device such that the device center is not on thebody and/or edges of the data acquisition device 201. A 180 degreeseparation can correspond to a configuration in which the first andsecond pressure sensors are diametrically opposed from one another.Angles less than 180 degrees can correspond to configurations in whichthe first and second pressure sensors are on opposite sides of thedevice, determined for example relative to a reference axis. Angles lessthan 180 degrees can correspond to configurations in which the first andsecond pressure sensors are on the same side of the device, determinedfor example relative to a reference axis. The first and second pressuresensors can be used to determine a side-to-side or a front-to-backpressure differential of the data acquisition device 201 (i.e., thepressure levels on the left side, right side, front side, and/or backside of the data acquisition device 201). Four pressure sensors can beused to determine side-to-side and/or front-to-back pressuredifferentials of the device when removably attached to a head. Theangles between sensors can be from about 1 degree to about 180 degrees,including every 1 degree increment within this range.

The data acquisition system 200 (e.g., the data acquisition device 201,a user's device, and/or a remote server) can determine whether the dataacquisition device 201 is properly or improperly positioned by analyzingthe pressure readings of the one or more pressure sensors. The dataacquisition system 200 can assess the quality of the data signals fromthe data acquisition modules 218 to ensure proper stability and contactof data acquisition modules 218 is occurring to ensure high quality datais being obtained by the data acquisition device 201. If properlypositioned, the data acquisition device 201 can automatically begincollecting data (e.g., immediately or after one or more additionalconditions are satisfied). The data acquisition device 201 can collectdata when not positioned properly, however, some of the data may haveaccuracy, precision and/or reliability issues, or some of the data maybe missing altogether (e.g., pulse oximetry data). The data acquisitionsystem 200 can notify the user that the data acquisition device 201 isnot positioned properly. Additionally or alternatively, the dataacquisition system 200 can be configured to determine whether the dataacquisition device 201 is properly positioned by measuring the voltagedrop across one or more sensors of the data acquisition modules 218).

The data acquisition device 201 can begin collecting data when one ormore conditions are satisfied (e.g., 1 to 5 or more conditions). Thedata acquisition device 201 can begin collecting data when a properposition is detected. The data acquisition device 201 can begincollecting data when the data acquisition system 200 detects that theuser is in a sleeping position and/or when the user is in a sleepinglocation, for example, for a predetermined amount of time (e.g.,immediately (no time), or after 1 min to 5 min or more have elapsed).The sleeping location can be established or otherwise settable by theuser. For example, the data acquisition device 201 can begin collectingdata after first, second, third, and/or fourth conditions are satisfied.The data acquisition device 201 can begin collecting data immediatelyafter any one condition or combination of conditions is satisfied. Thefirst condition can correspond to correct device placement (e.g., of thedata acquisition device 201). The second condition can correspond touser input (e.g., selection of a command prompt). The third conditioncan correspond to a position of the device relative to the environment,for example, whether the orientation of the data acquisition device 201is in a position indicative of a sleeping position of the user (e.g.,lying down, either prone, supine, or on side). The fourth condition cancorrespond to a location of the user (e.g., on a bed). Sleep datacollection can begin when the pressure sensors detect that the dataacquisition device 201 is properly attached to a head or when the dataacquisition modules 218 begin collecting data.

The data acquisition device 201 can have one or more temperature sensors(e.g., 1, 2, 3, 4 or more temperature sensors) configured to monitor auser's body temperature. The temperature sensors can be temperaturetransducers (e.g., thermocouples). The temperature sensor can beattached to or integrated with the data acquisition device 201. Thetemperature sensors can be configured to detect when the dataacquisition device 201 is attached to a head, for example, by detectinga body temperature. An environment temperature sensor can be configuredto measure environmental temperature. The environment temperature sensorcan be one of the temperature sensors of the data acquisition device201. The environment temperature sensor can be a temperature sensor of asleeping location (e.g., house or apartment). The data acquisitionsystem 200 can determine a user's optimum sleeping temperature andsuggest a sleeping temperature for the user, for example, from about 60degrees Fahrenheit to about 85 degrees Fahrenheit, including every 1degree increment within this range.

The data acquisition system 200 (e.g., the data acquisition device 201and/or the data acquisition modules 218) can have one or moreaccelerometers (e.g., one accelerometer). The accelerometer can beattached to the data acquisition device 201 or can be wirelesslyconnected (e.g., located at the subject's wrist, finger, or otherlocation). In some aspects, the accelerometer can detect limb movementsof the subject. The accelerometer can detect a user's positional state,for example, a user's movement. The accelerometer can be a two-axisaccelerometer. The accelerometer can be a three-axis accelerometer. Theaccelerometer can be configured to detect head, body, and/or limbmovements, or any combination thereof. The accelerometer can be used todetect lack of movement as well, for example, the length of time in asingle position without movement or with movement within a specifiedtolerance (e.g., voltage level or movement amount, for example, 5 cm orless).

The electronics modules (e.g., data acquisition modules 218) caninclude, for example, three channels of frontal EEG and one EEGreference sensor to detect brain wave activity, a heart rate sensor tomonitor cardiac activity (e.g., RR variability), an accelerometer (e.g.,two or three axis accelerometer) to detect head, body, and/or limbmovements, or any combination thereof.

The electronics module 218 can be configured to contact a user's skin(e.g., a user's forehead) during use. The data acquisition device 201can press the EEG sensors and/or ECG sensor(s) against the user's skin(e.g., forehead) when secured to the head 208, for example, with anelastic fit or with an interference fit. Alternatively or additionally,the sensors can be adhered to the user's skin (e.g., forehead) using anadhesive with or without the data acquisition device 201.

The electronics module 218 can be configured to measure brain activity,for example, during light sleep, during rapid eye movement (REM) sleep,during slow-wave sleep (SWS) (also referred to as deep sleep), or anycombination thereof. The electronics module 218 can be configured tomeasure cardiac activity, for example, HRV such as RR intervals. Theelectronics module 218 can be configured to detect a user's motionand/or a user's lack of motion.

The electronics module components (e.g., channels, sensors,accelerometers) can be attached to or integrated with the dataacquisition module 218. The data acquisition module 218 can bepermanently attached to, removably attached to, or integrated with thedata acquisition device 201 (e.g., to and/or with the body 214).Additionally or alternatively, the various activity-measuring components(e.g., channels, sensors, accelerometers) can be attached to orintegrated with an attachment portion of the data acquisition device201, for example the body 214 separate and apart from the module 218.The module 218 can be interchangeable with one or more other modules(not shown) having a different number of sensors, one or more differenttypes of sensors, or otherwise having at least one differentparameter-measuring capability relative to the electronics module 218.The module 218 can be interchangeable with another module having thesame exact module or otherwise with another module having the same exactparameter-measuring capabilities. Different modules 218 can havedifferent sizes relative to one another. Different modules 218 can havedifferent shapes relative to one another.

The data acquisition device 201, a user device, and/or a remote servercan analyze the sleep data collected, as described in further detailbelow. The data acquisition device 201, the user device, and/or a remoteserver can determine one or more parameters from the data collected, forexample, using one or more programmable processors. The parameters caninclude total light sleep, total SWS (also referred to as total deepsleep), total REM sleep, total non-REM sleep (total light sleep andtotal SWS added together), total sleep (total REM and non-REM sleepadded together), longest deep sleep duration, deep sleep amplitude,strength of deep sleep, heart rate, heart rate variability, total timein bed, time to fall asleep, time awake between falling asleep andwaking up, various sleep microstructure features (e.g., number of sleepslow oscillation (SO) events described in further detail below), or anycombination thereof. The time-based parameters (e.g., the “total,”“duration,” and “time” parameters) can be measured in the time domain,for example, using seconds, minutes, hours. Days, weeks and years can beused for accumulated and/or running totals.

The total time in bed parameter can be measured from a start point to anend point. The start point can correspond to when the user manuallyactivates the data acquisition device 201, for example, by selecting astart instruction (e.g., “ready to sleep”) on the display 22. The startpoint can correspond to when the data acquisition device 201 isactivated (e.g., automatically or manually). The data acquisition device201 can be automatically activated, for example, when a voltage isdetected across two or more sensors of the module 218 (e.g., across twoor more of the EEG electrodes). The voltage can indicate contact withskin and cause the data acquisition device 201 to begin measuring thetotal time in bed. The data acquisition device 201 can have a timer. Thedata acquisition device 201 can be automatically activated whenpositioned on the head 208 as a result of one or more pressure sensorsexceeding a pressure threshold. The end point can correspond to when theuser manually deactivates the data acquisition device 201, for example,by selecting an end instruction (e.g., “turn off alarm” or “get sleepreport”) on the display 22. The end point can correspond to when thedevice is automatically deactivated. The data acquisition device 201 canbe automatically deactivated, for example, when the accelerometerindicates the user is walking around or has taken the data acquisitiondevice 201 off their head.

The data acquisition system 200 can provide audio stimulation (alsoreferred to as audio entrainment) using, for example, one or more soundwave generators 217 (e.g., 1 to 4 sound wave generators). The sound wavegenerators can be, for example, speakers. A portion of the dataacquisition device 201 can be positionable over and/or engageable with aleft and/or right ear of a user such that the sound wave generators 217can emit sound into a user's ears. The sound wave generators 217 can beattached to, embedded in, or integrated with the device body. The soundwave generators 217 can be in wired or wireless communication with thedata acquisition device 201, a user device, a remote server, or anycombination thereof. The sound wave generators 217 can be microspeakers.

Additionally or alternatively, the data acquisition system 200 canprovide audio stimulation via bone conduction by transmitting soundsignals through bone to a user's inner ear. The data acquisition system200 can have one or more actuator assemblies 213 to provide boneconduction sound transmission. The actuator assemblies 213 can have anactuator (e.g., a transducer). The actuator can be vibratable (e.g., theactuator can be configured to vibrate). The actuator assemblies 213 canhave a transceiver coupled to the actuator. The transceiver can causethe actuator to vibrate to generate sound, for example, when thetransceiver is electronically driven with sound signals (e.g., from adriver and/or a controller, for example, from the data acquisitiondevice 201). The actuator can be a piezoelectric actuator. Thepiezoelectric actuator can be configured to move a mass to provide soundthrough bone. The actuator assemblies 213 (e.g., the actuator) can bepositioned near the ear and/or on the cheek. For example, the actuatorassemblies 213 can be positioned on a user's skin proximate thezygomatic bone, the zygomatic arch, the mastoid process, or anycombination thereof. The data acquisition system 200 can have 1 to 6actuator assemblies, or 1 to 6 actuators, including every 1 actuatorassembly/actuator increment within these ranges.

The data acquisition system 200 can provide visual/optical stimulation(also referred to as light entrainment) using, for example, one or morelight emitting sources 219 (e.g., 1 to 20 light emitting sources). Aportion of the data acquisition device 201 can be positionable overand/or engageable with a left and/or right eye of a user such that thelight sources 219 can emit light into a user's eyes (e.g., through theuser's closed eyelids). The data acquisition device 201 can beconfigured to partially or completely cover one or both eyes. The dataacquisition device 201 can be configured for temporary securement aboveor proximate to a user's eyes/eyelids. For example, a portion of thedata acquisition device 201 can be configured to rest against and/oradhere to an eyebrow, the area proximate an eyebrow, the glabella, thenose (e.g., dorsal bridge, dorsal base, tip), cheek, or any combinationthereof. The light sources 219 can be attached to, embedded in, orintegrated with the data acquisition device 201.

The data acquisition system 200 can provide audio entrainment, opticalentrainment, cranial electrotherapy stimulation (CES), or anycombination thereof, in addition to or in lieu of the data collectionand associated analyses described below.

FIG. 3 shows an example system 300 for determining metrics of a subject,such as a brain age or a brain health score for a user of a head-wornsensing device (e.g., data acquisition device 201) as previouslydescribed. In this system, one or more training subjects 302 providetraining data through physiological sensors 304 and user interfaces 306(either directly or by another user such as an administrator orhealth-care provider), which can be combined with tagging data 308 bytraining computing hardware 310 to generate one or more function-metricclassifiers 312. Operating subjects 314 can then use operating computinghardware 316 to collect data through physiological sensors 318 and/oruser interfaces 320 to generate one or more function metrics 322.

Training subjects 302 are a group of subjects (e.g., human or otheranimals) that contribute data to be used as training data. For example,the subjects 302 may be patients who, under a program of informedconsent, provide some of their medical records for research purposes. Inanother example, the subjects 302 may be generally healthyrepresentatives of a population that have agreed to contribute trainingdata. The training subjects 302 may be organized by physiological (e.g.,healthy vs having a known medical issue, menopausal status, menstrualcycle phase), demographic details (e.g., age, gender, geographiclocation, location of residence, education level, professional level),and/or groups based on lifestyle factors (e.g., activity level, past orcurrent behaviours). Thus, classifiers 312 may be created for thepopulation as a whole, or for particular subpopulations (e.g.,stratified by health status, age, or other factors expected to impactthe operation of the classifiers). In some cases, each classifier 312may be personalized, using a single subject 302 to create or modify aclassifier, where the training subject 302 is also the operating subject314 so that their personal classifier is used later in operation.

Physiological sensors 304 include one or more sensors that can sense oneor more physiological phenomena of the subjects 302. In some cases, thesensors 304 can include sensors mounted in a head-worn device such asthe data acquisition modules 218 of the data acquisition device 201 andthe physiological sensors 104 of the data acquisition device 101.However, other arrangements are possible such as bespoke trainingsensors used only for the collection of training data, or use of datacollected with other sensors for other purposes (e.g., use of some of,but not all, data generated in clinical sleep studies).

The user interface 306 can include hardware and corresponding softwareto present user interfaces to a user (e.g., subject 302 or another user)to collect data about the subject 306. This can include the demographicdata described, can present information to the subject 302 about the useof data collected and aid in the development of informed consent, etc.The user interface 306 can include a personal computing device such as adesktop or laptop, a mobile computing device such as a phone, tablet,raspberry pi or other appropriate elements for user input and output.

Tagging data 308 includes data that annotates data from thephysiological sensors 304 and/or the user interface 306. For example, auser (shown or not shown) and/or an automated system (shown or notshown) can annotate data from the physiological sensors 304 to mark thesubject 302 as in states such as sleep-states. These tags in the taggingdata 308 can also include other data that can be used in the creation ofthe classifiers.

The training computer hardware 310 can receive the tagging data 308,data from the physiological sensors 304, and/or data from the userinterface 306 to generate one or more function-metric classifiers 312.Example processes for such classifier creation are described in greaterdetail elsewhere in this document. The classifier 312 can, given aparticular set of inputs, generate one or more functional metrics. Thesefunctional metrics can include values that give an indication of thestate and/or processes of training subjects 302 while they are beingmonitored with the physiological sensors 304. One example functionalmetric is brain age, which can include an indication of a subject'sbrain function as compared to their expected brain function based ontheir chronological age and functional metrics acquired from a pluralityof subjects, though other types of metrics can be created. Anotherexample functional metric is sleep quality and sleep quality relatedmetrics such as an amount of slow wave sleep (SWS), stress recovery(e.g., pre-sleep stress level and post-sleep stress level). Slow-wavesleep (SWS) can be referred to as deep sleep, and can include stagethree of non-REM sleep. SWS can include both stage 3 non-REM sleep andstage 4 non-REM sleep. SWS can include one of stage 3 non-REM sleep andstage 4 non-REM sleep.

Later, after the classifier has been created, one or more operatingsubjects can use the physiological sensors 318 and/or user interface 308to provide new data to the operating computing hardware 316. Thecomputing hardware can use this new data with the classifier 312 tocreate new functional metrics for the operating subjects. Said anotherway, the users 314 can wear a headband (e.g., data acquisition device201) to bed as previously described, and they can be presented with abrain age or other metric created from one sleep session or from a groupof sleep sessions (e.g., a weeks' worth of sleep). As will beappreciated, this can advantageously provide the users 314 with theability to assess their functional metrics in a single night's sleep orover multiple nights' sleep to provide a short-term result based on asingle night's sleep or a longer term result based on multiple nights'sleep data that can be compared to identify trends, changes over time,and can be aggregated to mitigate the night-to-night variability invalues of the measured physiological phenomena. The users 314 can alsobe provided with assessments that are consistent with early signs ofcognitive decline.

For example, an otherwise healthy user 314 can use a sleep wearable(e.g., data acquisition device 201) to assess their cognitive functionperiodically to assess their brain function. The user 314 could use thesleep wearable over the course of several years where they exhibit anormal brain age progression. The user 314 could all of the sudden see adramatic increase in their brain-age progression. Even without othersymptoms, they can then go to their doctor, who can run clinicalsleep-study or conduct an MRI scan to discover one or more earlydeveloping signs of neurodegenerative diseases and/or neurodegenerativedisorders.

Additionally, the determined brain age from the system 300 can beobtained and provided to the subject 314 when the subject is in theirnormal sleep environment (e.g., at home). This provides data to the userwhen they are not disturbed by anxiety, not in a comfortable bed orfamiliar environment, or other factors that would impact their abilityto sleep normally. The system 300 does not involve a technicianinstalling multiple uncomfortable leads on the subject 314 which can beadvantageous for the subject 314 because the system 300 is much moreaccessible as well as being more time and cost effective than asleep-study or other brain function tests that take place in a clinicalenvironment that can include several leads attached to a subject thatcan have an impact on the subject's ability to achieve sleep resultsthat are representative of the subject's normal sleep results.Additionally, the system 300 can be self-administered by the subject,without the intervention of a technician, while a sleep-study and a MRIinclude the intervention of a technician to assist in the testingprotocol. The system 300 can provide a self-diagnostic test thatprovides the subject with a functional result that a sleep-study or MRIcould not provide.

The system 300 can also be used to monitor effectiveness of clinical orwellness interventions. For example, the system 300 can assess if amedication, drug, or other intervention slowed the progression of brainage for the subject 314. The system 300 can assess if taking upmeditation, improving sleep hygiene, exercising, or joining peer groupshave an impact on the progression of brain age. The system 300 canassess if a combination therapy (e.g., administration of a drug Alongwith digital health intervention) has a greater impact on brain age thanthe impact of each individual intervention. The system 300 can provideauditory stimulation of sleep slow oscillations and assess the impact ofthe auditory stimulation on slow-wave sleep. In some aspects, the system300 can assess the potency of a drug, the effectiveness of a vaccine,and an immune-response of a subject. In some aspects, the system 300 canbe utilized in interventions related to behavioral and lifestyle changesincluding the reduction or elimination of tobacco (or other drug)consumption, changing eating habits, changing drinking habits, activitylevel, among others.

FIG. 4 shows an example process 400 that can be used to produceclassifiers that are able to evaluate subject data (e.g., sleep data)and generate functional metrics (e.g., brain age or brain health). Forexample, the process 400 can be performed by the elements of the system300 and will be described with reference to those elements. However,other systems can be used to perform the process 400 or similarprocesses.

Generally speaking, the process 400 includes data collection 402-404,feature engineering 406-410, and machine learning training 412-414. Inthe data collection 402-404, data is gathered in formats in which it isgenerated or transmitted, then reformatted, decorated, aggregated, orotherwise processed for use. In the feature engineering 406-410, data isanalyzed to find those portions of the data that are sufficientlypredictive of a physiological function (e.g., brain function) to be usedto train the classifiers. This can allow the discarding of extraneous orunneeded data, improving computational efficiency and/or accuracy. Themachine learning training 412-414 can then use those features to buildone or more models that characterize relationships in the data for usein future classifications.

In the data acquisition 402 for example, the computing hardware 310 cancollect data from the sensors 304, from the user interface 306, and thetagging data 308. As will be understood, this acquisition may happenover various lengths of time and some data may be collected after otherdata is collected.

In the preprocessing and classifying 404 for example, the computinghardware 310 can perform operations to change the format orrepresentation of the data. In some cases, this may not change theunderlying data (e.g., changing integers to equivalent floating pointnumbers, marking epochs of time in time-series data), may destroy someunderlying data (e.g., reducing the length of binary strings used torepresent floating point numbers, applying filters to time-series data),and/or may generate new data (e.g., averaging two frontal EEG channelssuch as Fp1 and Fp2 to create a single, virtual prefrontal EEG signal;mapping annotations to the data).

In the feature extraction 406 for example, the computing hardware 310can extract features from the processed and classified data. Some ofthese features can be related to sleep macrostructure, i.e. thedecomposition of a sleep session into sleep stages (e.g., informationabout the proportion of sleep spent in a particular stage and the numberof transitions between different sleep stages). Some of these featurescan be related to sleep microstructure, i.e. information about a sleepsession based on the detection and analysis of specific waveforms andfrequency components of the acquired data (e.g., number of SO events,information related to sleep spindles and SO-spindle coupling, averagesof various EEG frequency components within a sleep stage, like averagedelta band (0.5-4 Hz) power during SWS or strength of deep sleep). Someof these features can be related to cardiac activity (e.g., heart rate,heartrate variability (HRV)). Some of these features can be related torespiratory activity and/or blood oxygenation (e.g., apnea-hypoxia index(AHI)). Some of this data can be related to stimulus-response activity(e.g., response by the subject to light, sound, electricity, or otherstimulus including while asleep). Additionally, sleep microstructurefeatures can include total numbers and stage-specific densities (i.e.,number of events during a specific sleep stage divided by total durationof a specific sleep stage) of Sos, fast spindles, slow spindles,early-fast spindles, and late-fast spindles. Sleep microstructurefeatures can include—Averages, standard deviations, and otherstatistical descriptives of SO morphological features (e.g., SOpeak-to-peak amplitude, SO duration, SO negative peak amplitude). Sleepmicrostructure features can include averages, standard deviations, andother statistical descriptives of spindle features (e.g. spindlefrequency, spindle duration, spindle amplitude). Sleep microstructurefeatures can include averages, standard deviations, and otherstatistical descriptives of SO-spindle coupling features (e.g. relativeSO-spindle phase, overlap between a spindle event and the SO up-phase),among other microstructure features and other microstructure featurecombinations.

In the feature transformation 408 for example, the computing hardware312 can modify the features in ways that preserve all data, destroy somedata, and/or generate new data. For example, values may be mapped to agiven scale (e.g., mapped to a log scale, mapped to scale of 0 to 1). Insome cases, statistical aggregates can be created (e.g., mean values,standard variation). These aggregates may be generated from data foreach sleep session, across the detected sleep stages, or may aggregatedata across multiple sleep sessions.

In the feature selection 410 for example, the computing hardware 312 canselect some of the features for use in training the model. This caninclude selecting a proper subset (e.g., some, but not all) of thefeatures.

In the model training 412 for example, the computing hardware 312 cantrain one or more machine-learning models using the selected features.In some cases, one or more models are created that propose mappingsbetween the features and tagged data indicating brain age for thosefeatures. Then, the computing hardware 312 modifies those mappings toimprove the model's accuracy.

In the output evaluation 414 for example, the computing hardware 312 cangenerate one or more functions, sometimes called classifiers, whichinclude a model. This inclusion can involve including the whole model,or may involve only including instructions generated from the modelallowing for the classifier to have a smaller memory footprint than themodel itself.

Described now will be one example implementation of the process 400.While a particular number, type, and order of details are selected forthis implementation, it will be understood that other numbers, types,and order of details may be used to implement the process 400 or otherprocesses that accomplish the same goals.

In the data acquisition 402 in this implementation, basic informationabout a subject can be acquired such as one or more of a subject's: nameor identification (ID), age, gender, other sociodemographic data,health-related information, physiological information (e.g., menopausalstatus and menstrual cycle information), and information related tolifestyle or behaviors (including but not limited to sleep habits,tobacco/alcohol consumption, exercise, meditation, among others). Thisdata can be user inputted or integrated from other devices.

The system(s) described above can enable the real-time open-loop orclosed-loop delivery of stimuli, which include at least one or more ofaudio, light, vibratory, or electrical stimuli, as part of the dataacquisition procedure.

The subject's raw biological information can be collected, which caninclude of at least one or more sleep recordings where each recordingincludes at least one frontal EEG channel and can include additionalsensors such as: additional EEG channels, forehead PPG, blood oxygensaturation (SpO₂), EMG, EOG, EDA, and actigraphy (movement) sensors. Insome cases, systems can use two frontal EEG channels (Fp1 and Fp2), withor without PPG, SpO₂, and actigraphy. The data can be collected fromfull night recordings and/or less-than full nights (e.g., naps).

The output of the data acquisition 402 can include each subject's rawbiological signals and stimulus types and timings, from one or multiplerecordings, as well as the subject's age and other basic information.

In the preprocessing and classifying 404 in this implementation,preprocessing and automated sleep-stage classification can includevarious operations that may be performed on the output of the dataacquisition 402. For example, two bandpass-filtered (0.2-40 Hz) frontalEEG signals are averaged to obtain a single virtual frontal EEG channel.Heartbeat times are extracted from the filtered and demodulated PPGsignal using peak detection.

The data is segmented into discrete overlapping and/or non-overlappingepochs and each epoch is described using a set of time-domain,frequency-domain and other EEG features typically used for sleep stageclassification. Each epoch is classified as either wakefulness (W),rapid eye movement (REM) sleep, non-REM sleep stage 1 (N1), non-REMsleep stage 2 (N2), or deep sleep (N3), using an automatic sleep stageclassification algorithm based on machine learning (ML) and theextracted sleep EEG features.

Manually annotated sleep stages can be used from a PSG database, butother implementations can use automatically-scored stages. The sleepstage classification algorithm may or may not use actigraphy and HRVdata in addition to EEG data.

The output of the preprocessing and classifying 404 can include eachsubject's preprocessed biological data, from one or multiple recordings,segmented into time-based epochs. For example, the time-based epochs canbe segmented into 10-second, 20-second or 30-second epochs, where eachepoch is annotated by a sleep stage, or with probabilities of belongingto each of the possible sleep stages (i.e. softmax output).

In the feature extraction 406 in this implementation, sleepmacrostructure features are computed. These macrostructure features arerelated to stage durations/percentages, stage transition probabilities,and fragmentation or awakenings. In an implementation whereneural-network-based sleep stage probabilities (i.e. hypnodensitydiagram) are available for each epoch, macrostructure features caninclude hypnodensity-based features, e.g. the maximum probability ofwakefulness in a recording, or the maximum value of the product betweenthe N2 and REM probability in a recording.

From the full preprocessed EEG signal, and the obtained sleep stageannotations, EEG-based stage-specific sleep microstructure features arecomputed. The microstructure features include stage-specific EEGfeatures such as stage-specific averages, std deviations, and otherstatistical descriptives of the time-domain, frequency-domain and otherEEG features.

From the full preprocessed EEG signal, and the obtained sleep stageannotations, waveform-specific EEG based microstructure features arecreated, related to slow oscillations (Sos), sleep spindles, SO-spindlecoupling and density, and other EEG waveforms relevant in the context ofaging. From the full preprocessed EEG signal, and the obtained sleepstage annotations, stimulus-response-related EEG features are createdwhich are calculated by analyzing the EEG responses to stimuli whichinclude at least one or more of audio, light, vibration, or electricalstimulation, which are delivered through the headband either inopen-loop or in closed-loop while the subject is asleep.

From the obtained heartbeat times, and the obtained sleep stageannotations, an array of both stage-specific and general heart ratevariability (HRV) features are computed, based on time-domain,frequency-domain and nonlinear HRV analysis. From the collected 402data, and the obtained sleep stage annotations, time-domain featuresrelated to blood oxygenation and sleep apnea are computed.

Some implementations use just sleep macrostructure features andEEG-based microstructure features (both stage-specific andwaveform-specific) and do not use stimulus-response-related EEGfeatures.

The output of the feature extraction 406 can include each of thesubject's recordings, either one or multiple, described with an array ofsleep macrostructure, as well as EEG-based, HRV-based and SpO₂-basedsleep microstructure features. Total number of features can be labeledM_(full).

In the feature transformation 408 in this implementation, the computedfeatures are transformed using a log-based transformation, in order toremove skewness in the feature distributions. For subjects with multiplerecordings, each feature value is determined as the mean feature valueacross either all or a subset of the available recordings. Based on thedates of specific recordings, and recording quality assessment (outlierdetection), the process determines which recordings to include incalculating the mean feature values. Additionally, when multiplesubjects with multiple recordings are available, new features related tonight-to-night variability in specific sleep features can be used asinputs to the BA model (e.g. std deviation of a given features acrossmultiple recordings for the given subject). Alternatively, instead ofaveraging the features for subjects with multiple recordings, finalestimate of the subjects' physiological metric (e.g. brain age) can bedetermined as the mean estimate of the subjects' physiological metricbased on either all or a subset of the available recordings.

In some cases, just one full night's recording per subject is used. Insome cases multiple night's recordings are used with feature averagingas the subject wears the headband for multiple nights or sleep sessions.

The output of the feature transformation 408 can include data for eachsubject described with M_(full) transformed sleep macrostructure andmicrostructure features, which are based on either one or multiplerecordings.

In the feature selection 410 in this implementation uses a sequentialfeature selection algorithm, or the “Maximum Relevance MinimumRedundancy” (MRMR) feature selection algorithm, and the availabletraining data, to select a subset of features to be included in thefinal feature set. The criterion used to determine the optimal featureset is the cross-validated mean absolute error (MAE) in predicting thesubjects' chronological age (CA). An example of the machine learning(ML) algorithm that can be used is the extreme learning machine (ELM)regressor with one or more hidden layers and the option of a functionallink. At each iteration of the sequential feature selection algorithm,the average of a repeated k-fold cross-validated MAE is determined. Themodel hyperparameters are optimized using a Bayesian optimizationalgorithm. At each iteration of the cross-validation procedure, featuresare normalized using mean and std. deviation values which are determinedbased on data from the train folds. For example, gradient boosted trees,Minimum Redundancy Maximum Relevance (mRMR) techniques may be used.However, other machine learning or non-machine learning techniques canbe used.

In some cases the ELM classifier and sequential feature selection onlyare used, but other machine learning and feature selection techniquesmay provide better results based on the particulars of the system andthe used training data. For example, other feature selection strategies(such as MRMR) using another error metric such as cross-validated rootmean square error (RMSE) may be used and other types of regressions maybe used such as other nonlinear regression models such as support vectorregression (SVR).

The output for the feature selection 410 can include each trainingsubject, described with M_(reduced) transformed sleep macrostructure andmicrostructure features, where M_(reduced)≤M_(full).

In the model training 412 in this implementation, a final set ofM_(reduced) features is used to train the prediction model. The modelhyperparameters are determined using the Bayesian optimizationalgorithm, with the goal of optimizing the average model performance inrepeated k-fold cross-validation. Multiple approaches are possible:classical regression; regression with a custom loss function based onresidual-label covariance analysis; regression with adjustment of theage-dependent bias, a deep label distribution learning algorithm basedon neural networks, a cascade of a multi-class classification model(e.g. a support vector machine) followed by several regression modelswhere each is trained for a specific demographic or physiological class(group), etc.

Using the cross-validated BA estimates from an entire dataset, or arelevant subset of the available data, as well as BA estimates obtainedin a left-out validation set and the subjects' chronological ages, aregression analysis of potential Brain Age Index (BAI) covariates isconducted. BAI is here defined as the difference between BA and CA.Potential BAI covariates include various demographic, lifestyle, andhealth-related variables, e.g. gender, race, having a sleep disorder,body mass index, drinking and smoking habits, cardiovascular healthvariables, psychological health variables, etc. Identification ofstatistically significant BAI covariates can be used to better informthe user and help provide personalized intervention recommendations

In the output evaluation 414 in this implementation, output of the modelis evaluated on new subjects. The subject's features are calculatedaccording to steps 402-408, and a set of M_(reduced) predefinedfeatures, which were chosen to be most relevant for brain age predictionby the ML part of the algorithm.

The trained brain age model from step 412 outputs a Brain Age estimateor other metric. In some cases, the output of the algorithm includes theestimated brain age. In some cases, a Brain Age estimate for a givensubject is determined by averaging algorithm's outputs for multiplerecordings from the same subject. In some cases, the output of thealgorithm includes a degree of confidence in the output. Based on thenumber of nights of data, the multi-night variability of the data, andthe tightness of the fit with the pre-built normative distributioncurves, or any combination thereof, a confidence level is provided foreach brain age prediction.

In some cases, the output of the algorithm includes excess brain age,which is the amount or percentage by which brain age exceedschronological age. For example, the user is provided with an explanationof the output, i.e. model interpretations. Model interpretations areprovided using explainable AI (XAI) techniques such as Shapley AdditiveExplanations (SNAP) and local interpretable model-agnostic explanations(LIME). A predefined number of most important features contributing tobrain age excess are shown to the user, each feature in terms of one ormore of the following: directionality and strength of contribution tobrain age excess, mean feature value, normative distribution of thespecific feature adjusted by age, gender, or other potentiallyconfounding factors (e.g., sociodemographic, physiological). Such outputcan be shown to a medical expert who could propose and guide futureinterventions aimed at modifying the sleep features which arecontributing to the objectively measured brain age excess. Based onmodel interpretation, and based on the user's demographic, health,lifestyle/behavioral variables, recommendations or tips can be providedto the user. Output to users can be in a variety of forms, including:mobile app, web app, emailed report, and others.

FIG. 5 shows an example process 500 that can be performed by the system300. In the process 500, data is acquired 502 e.g., through a headband.A closed-loop stimulation algorithm is executed 504 to provide a subjectwith stimulation. Times and types of stimuli are recorded 506. Data isprocessed and segmented into epochs 508. Sleep stages in the data areclassified by using an automated sleep stage classification algorithm510. Artifacts are removed in the data and features are extracted 512. Alog transformation is applied to at least some of the features 514.Basic information about the subject is collected 516. Sleep features andbasic information are combined 518. If more recordings for the subjectare available, they are added to a database of prior recordings 522.Suitable recordings are selected 524. Between-night features areaveraged 526. If more recordings for the subject are not available, anew database entry is created for the new subject and the recording isadded 520. It is determined if the subject is used in the training set528. If the subject is in the training set, features are selected 530.Machine learning hyperparameters are optimized 532. Features arestandardized 534. One or more models are trained 536. A brain age modelis created with defined input features and z-score parameters 538. Ifthe subject is not in the training set, an output is predicted 540. Itis determined if the brain age excess exceeds a threshold 542. If thethreshold is exceeded an XAI algorithm is executed on the data 544.Model interpretations are created 546. Feature values are comparedagainst normative distributions 548. Confidences is assessed 550. Areport is made to a user 552.

FIG. 6 shows an example process 600 for determining metrics of asubject. The process 600 can be performed, for example, by thephysiological sensors 304, a data source 602, the training computerhardware 310, and the operations computer hardware 316, though othercomponents may be used to perform the process 600 or other similarprocesses.

The sensors 304 sense physiological measures 604 and send thephysiological measures to the training computer hardware 310. Thetraining computer hardware 310 receives the physiological measures ofthe subject recorded at least partly while the subject is asleep. Forexample, one or more subjects can be sensed to build training data forthe process 600. The physiological measures of the subject can include avariety of data, including a frontal electroencephalography (EEG)channel, a prefrontal (Fp) EEG channel, two frontal EEG channels, twoprefrontal EEG channels, forehead photoplethysmography (PPG), bloodoxygen saturation (SpO2), electromyography (EMG), electrooculography(EOG), electrodermal activity (EDA), and actigraphy data collected, forexample, by one or more worn devices that are worn while the subjectssleep.

In some cases, the physiological measures of the subject were recordedat least partly while the subject is asleep. This can include instanceswhere one or more devices provided the subject with at least onestimuli. The type of stimuli can include, but is not limited to, audiostimuli, light stimuli, electrical stimuli, open-loop stimuli, andclosed-loop stimuli; and the physiological measures comprising timinginfo defining timing of stimuli provided to the subject

The data source 602 provides demographic data 608 to the trainingcomputer hardware 310 and the training computer hardware 310 receivesthe demographic data for the subject. For example, the data sources 602may include a database stored in one or more servers connected to thetraining computer hardware 310 over a data network such as the internet.

In some cases, the demographic data includes a chronological age for thesubject when the physiological measures were recorded. This can berecorded, for example, in terms of years, months, days, etc., thoughother formats are possible. The demographic data can also oralternatively include data for subjects such as an identifier (theirlegal name, a unique identifier, etc.), a gender, sociodemographic data,health data, behavioral, and/or lifestyle data that have been entered bythe subject into an input device.

The training computer hardware 312 generates 612, using thephysiological measures and from the demographic data, segmentedtraining-data. The segmented training-data can specify a plurality ofepochs of time and data for the subject in each epoch. For example, eachepoch may be defined as a time window of 15 seconds, 30 seconds, 1.125minutes, etc. The epoch may overlap or may be separate such that they donot overlap and may begin at the ending of a previous epoch or after aperiod of time without an epoch defined.

To generate the segmented data, the hardware 312 can apply one or moreband-pass filters to at least some of the physiological measures, e.g.,to remove high and low values greater than and less than giventhresholds. At least frontal EEG signals can be combined to create avirtual frontal EEG signal. Heartbeat times can be extracted from PPGsignals by detecting peaks in the PPG signals. The physiologicalmeasures can be separated and/or segmented into a plurality of epochs oftime such that each epoch includes the various measures of physiologicalfunction that occurred in the subject concurrently. For each epoch oftime, features are generated that can include multiple time-domainfeatures, frequency domain features, and other non-linear or complexsignal descriptives (e.g., fractal dimension Lyapunov exponent, entropymeasures, histogram-based features).

The hardware 312 can use tagging data that tags each epoch of time witha sleep stage. Various schemes for defining sleep stage can be used. Inone scheme, the sleep stages are classified as wakefulness, rapid eyemovement (REM) sleep, non-REM sleep stage 1 (N1), non-REM sleep stage 2(N2), and non-REM sleep stage 3 (N3) and could include non-REM sleepstage 4 (N4). In one scheme, the sleep stages are classified as awake,REM, light sleep, and deep sleep. In one scheme, the sleep stages areclassified as awake, REM and nREM. In these schemes, an epoch can betagged as an unknown (not tagged) sleep stage, due to data loss, lowdata quality, or other reasons which would not allow the properfunctioning of the sleep stage classification algorithm.

The training computer hardware 310 generates 614, using the segmentedtraining-data, sleep-structure features for the subject. For example,the features of sleep-structure may conform to common sleep-structuretypes well known in the community (e.g., number of sleep SO events). Insome cases, the features of the sleep-structure may include or onlyinclude otherwise-unknown structure types developed for this technology.Sleep-structure features can include, but are not limited to,macrostructure features, microstructure features, physiological features(e.g., cardiac features, respiratory features), and combinationsthereof.

Macrostructure features are determined for the subject describingaspects of sleep including, but not limited to sleep-stage duration,sleep-stage percentage, sleep-stage transition probability, sleepfragmentation, and awakenings. Microstructure features are determinedfor the subject describing aspects of sleep including, but not limitedto stage-specific EEG features, waveform-specific EEG features, andstimulus-response EEG features. Cardiac features are determined for thesubject describing aspects of cardiac activity including, but notlimited to heartbeat times and tagging data that tags epochs of timewith sleep stage. Cardiac features can include time-domain,frequency-domain, nonlinear and complex HRV features, as well asstage-specific averages, standard deviations, and other statisticaldescriptives of those features.

Respiratory features are determined for the subject describing aspectsof respiratory activity for the subject including, but not limited toblood oxygenation and sleep apnea, using at least one of the groupconsisting of blood oxygen saturation (SpO2) data, heartbeat times, andthe tagging data. Respiratory features can include respiratory activityand/or blood oxygenation features (e.g. apnea-hypopnea index (AHI),respiratory rate, deoxygenation level), as well as stage-specificaverages, standard deviations, and other statistical descriptives ofthose features.

The training computer hardware 310 selects 616 a subset of thesleep-structure features as selected features. For example, one or moreanalyses may be performed to identify identifying the subset of thesleep-structure features as those features most predictive of thechronological age of the demographic data.

This selection can include transforming one or more features such asmacrostructure features, microstructure features, cardiac features, andrespiratory features. In some cases, sleep-structure features can beaggregated from multiple sleep-sessions. In some cases, sleep-structurefeatures are generated from only a single sleep session. To select thefeatures, cross-validated mean absolute error (MAE) (e.g., finding andaveraging the difference, without regard to the sign of the differences)with an extreme learning machine (ELM) regressor (e.g., using afeedforward neural network such as those with hidden nodes havingparameters that are not tuned), or some other regression model suitablefor the task, may be performed.

The training computer hardware 310 generates 618 one or morefunction-metric classifiers comprising training a model that defines atleast one relationship between the physiological measures and thechronological age. For example, the model may predict new results basedon old training data. The training can include determininghyperparmeters of the model or hyperparameters that control learningprocesses for a model using a Bayesian optimization algorithm. Thisoptimization algorithm can be configured to target various targets orloss functions, such as a model's performance in repeated k-foldcross-validation. The training can use, for example, a regression; aregression with a loss function based on a residual-label covarianceanalysis, or a deep label distribution algorithm.

The training can include refining the model reduce age-dependent bias.For example, it may be the case that some implementations may use modelsthat exhibit a mathematical bias (e.g., generation of an output set inwhich data incorrectly skews, clusters, or oscillates around one or moreattractor point in the output space, or that applies an weighting to aparameter or set of parameters that is either greater or smaller thanthe weighting exhibited by the ground truth) related to age or anotherdemographic criteria. In such a case, the training of the model caninclude refining or other editing in a way that reduces the bias alongthis parameter or multiple parameters. This refining can include firstidentifying a parameter for which the model exhibits bias, then applyingone or more modifications to the model and/or model output to reduce oreliminate. For example, it may be determined that the model performswell for users of a given age (e.g., 24 years and older) but less wellfor younger users (e.g., producing brain age estimates that are too highfor users of 0 years to 24 years, with error increasing the closer to 0years). In such a case, an output-conditioning function can be appliedto all outputs or outputs for users of age 24 years or less. One suchadjustment includes a linear adjustment (e.g., finding a constant valuec, and multiplying that constant value by 24—the age of the subject, andthen subtracting this value from the model's initial brain ageestimate). However, other adjustments are possible including non-linearscaling.

The training computer hardware 310 distributes 620 the function-metricclassifiers to a plurality of user devices (e.g., operating computerhardware 316) that are receive the classifier 622 and are configured tosense new physiological measures of other subjects at least partly whilethe other subjects are asleep. For example, with this classifiercreated, a manufacturer of a device such as a headband can include theclassifier in the computing hardware of the headband or an applicationassociated with the headband to run on a phone, computer, or otherdevice.

The operating computer hardware 316 receives 624, as input, newphysiological measures. For example, a new user may purchase or be giventhe headband, place the headband on their head, and then go to sleep asnormal at night. The hardware 316 can receive, from one or more sensors,new physiological measures of the subject recorded at least partly whilethe subject is asleep and recorded after the function-metric classifiershave already been generated.

The operating computer hardware 316 provides 626, as output, afunction-metric value determined based on the defined relationshipbetween the physiological measures and the chronological age. Forexample, the user may be provided with a brain age or neurologicactivity report showing the functional metric (e.g., brain age orotherwise) on a computer screen, via a mobile application, or in aprinted report.

To create this metric for the user, the hardware 316 can submit the newphysiological measures to at least one of the function-metricclassifiers as the input; and receive as output from the at least onefunction-metric classifier the function-metric value. In addition to asingle metric, the classifier can also provide other types of outputincluding but not limited to a confidence value, avariance-from-chronological-age value, a model interpretation, ahuman-readable instruction displayable to a user of an output device,and an automation-instruction that, when executed by an automated devicecauses the automated device to actuate.

FIG. 7 shows an example of a computing device 700 and an example of amobile computing device that can be used to implement the techniquesdescribed herein. The computing device 700 is intended to representvarious forms of digital computers, such as laptops, desktops,workstations, personal digital assistants, servers, blade servers,mainframes, and other appropriate computers. The mobile computing deviceis intended to represent various forms of mobile devices, such aspersonal digital assistants, cellular telephones, smart-phones, andother similar computing devices. The components shown here, theirconnections and relationships, and their functions, are meant to beexemplary only, and are not meant to limit implementations of theinventions described and/or claimed in this document.

The computing device 700 includes a processor 702, a memory 704, astorage device 706, a high-speed interface 708 connecting to the memory704 and multiple high-speed expansion ports 710, and a low-speedinterface 712 connecting to a low-speed expansion port 714 and thestorage device 706. Each of the processor 702, the memory 704, thestorage device 706, the high-speed interface 708, the high-speedexpansion ports 710, and the low-speed interface 712, are interconnectedusing various busses, and can be mounted on a common motherboard or inother manners as appropriate. The processor 702 can process instructionsfor execution within the computing device 700, including instructionsstored in the memory 704 or on the storage device 706 to displaygraphical information for a GUI on an external input/output device, suchas a display 716 coupled to the high-speed interface 708. In otherimplementations, multiple processors and/or multiple buses can be used,as appropriate, along with multiple memories and types of memory. Also,multiple computing devices can be connected, with each device providingportions of the necessary operations (e.g., as a server bank, a group ofblade servers, or a multi-processor system).

The memory 704 stores information within the computing device 700. Insome implementations, the memory 704 is a volatile memory unit or units.In some implementations, the memory 704 is a non-volatile memory unit orunits. The memory 704 can also be another form of computer-readablemedium, such as a magnetic or optical disk.

The storage device 706 is capable of providing mass storage for thecomputing device 700. In some implementations, the storage device 706can be or contain a computer-readable medium, such as a floppy diskdevice, a hard disk device, an optical disk device, or a tape device, aflash memory or other similar solid state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations. A computer program product can be tangibly embodied inan information carrier. The computer program product can also containinstructions that, when executed, perform one or more methods, such asthose described above. The computer program product can also be tangiblyembodied in a computer- or machine-readable medium, such as the memory704, the storage device 706, or memory on the processor 702.

The high-speed interface 708 manages bandwidth-intensive operations forthe computing device 700, while the low-speed interface 712 manageslower bandwidth-intensive operations. Such allocation of functions isexemplary only. In some implementations, the high-speed interface 708 iscoupled to the memory 704, the display 716 (e.g., through a graphicsprocessor or accelerator), and to the high-speed expansion ports 710,which can accept various expansion cards (not shown). In theimplementation, the low-speed interface 712 is coupled to the storagedevice 706 and the low-speed expansion port 714. The low-speed expansionport 714, which can include various communication ports (e.g., USB,Bluetooth, Ethernet, wireless Ethernet) can be coupled to one or moreinput/output devices, such as a keyboard, a pointing device, a scanner,or a networking device such as a switch or router, e.g., through anetwork adapter.

The computing device 700 can be implemented in a number of differentforms, as shown in the figure. For example, it can be implemented as astandard server 720, or multiple times in a group of such servers. Inaddition, it can be implemented in a personal computer such as a laptopcomputer 722. It can also be implemented as part of a rack server system724. Alternatively, components from the computing device 700 can becombined with other components in a mobile device (not shown), such as amobile computing device 750. Each of such devices can contain one ormore of the computing device 700 and the mobile computing device 750,and an entire system can be made up of multiple computing devicescommunicating with each other.

The mobile computing device 750 includes a processor 752, a memory 764,an input/output device such as a display 754, a communication interface766, and a transceiver 768, among other components. The mobile computingdevice 750 can also be provided with a storage device, such as amicro-drive or other device, to provide additional storage. Each of theprocessor 752, the memory 764, the display 754, the communicationinterface 766, and the transceiver 768, are interconnected using variousbuses, and several of the components can be mounted on a commonmotherboard or in other manners as appropriate.

The processor 752 can execute instructions within the mobile computingdevice 750, including instructions stored in the memory 764. Theprocessor 752 can be implemented as a chipset of chips that includeseparate and multiple analog and digital processors. The processor 752can provide, for example, for coordination of the other components ofthe mobile computing device 750, such as control of user interfaces,applications run by the mobile computing device 750, and wirelesscommunication by the mobile computing device 750.

The processor 752 can communicate with a user through a controlinterface 758 and a display interface 756 coupled to the display 754.The display 754 can be, for example, a TFT (Thin-Film-Transistor LiquidCrystal Display) display or an OLED (Organic Light Emitting Diode)display, or other appropriate display technology. The display interface756 can comprise appropriate circuitry for driving the display 754 topresent graphical and other information to a user. The control interface758 can receive commands from a user and convert them for submission tothe processor 752. In addition, an external interface 762 can providecommunication with the processor 752, so as to enable near areacommunication of the mobile computing device 750 with other devices. Theexternal interface 762 can provide, for example, for wired communicationin some implementations, or for wireless communication in otherimplementations, and multiple interfaces can also be used.

The memory 764 stores information within the mobile computing device750. The memory 764 can be implemented as one or more of acomputer-readable medium or media, a volatile memory unit or units, or anon-volatile memory unit or units. An expansion memory 774 can also beprovided and connected to the mobile computing device 750 through anexpansion interface 772, which can include, for example, a SIMM (SingleIn Line Memory Module) card interface. The expansion memory 774 canprovide extra storage space for the mobile computing device 750, or canalso store applications or other information for the mobile computingdevice 750. Specifically, the expansion memory 774 can includeinstructions to carry out or supplement the processes described above,and can include secure information also. Thus, for example, theexpansion memory 774 can be provided as a security module for the mobilecomputing device 750, and can be programmed with instructions thatpermit secure use of the mobile computing device 750. In addition,secure applications can be provided via the SIMM cards, along withadditional information, such as placing identifying information on theSIMM card in a non-hackable manner.

The memory can include, for example, flash memory and/or NVRAM memory(non-volatile random access memory), as discussed below. In someimplementations, a computer program product is tangibly embodied in aninformation carrier. The computer program product contains instructionsthat, when executed, perform one or more methods, such as thosedescribed above. The computer program product can be a computer- ormachine-readable medium, such as the memory 764, the expansion memory774, or memory on the processor 752. In some implementations, thecomputer program product can be received in a propagated signal, forexample, over the transceiver 768 or the external interface 762.

The mobile computing device 750 can communicate wirelessly through thecommunication interface 766, which can include digital signal processingcircuitry where necessary. The communication interface 766 can providefor communications under various modes or protocols, such as GSM voicecalls (Global System for Mobile communications), SMS (Short MessageService), EMS (Enhanced Messaging Service), or MMS messaging (MultimediaMessaging Service), CDMA (code division multiple access), TDMA (timedivision multiple access), PDC (Personal Digital Cellular), WCDMA(Wideband Code Division Multiple ACCess), CDMA2000, or GPRS (GeneralPacket Radio Service), among others. Such communication can occur, forexample, through the transceiver 768 using a radio-frequency. Inaddition, short-range communication can occur, such as using aBluetooth, WiFi, or other such transceiver (not shown). In addition, aGPS (Global Positioning System) receiver module 770 can provideadditional navigation- and location-related wireless data to the mobilecomputing device 750, which can be used as appropriate by applicationsrunning on the mobile computing device 750.

The mobile computing device 750 can also communicate audibly using anaudio codec 760, which can receive spoken information from a user andconvert it to usable digital information. The audio codec 760 canlikewise generate audible sound for a user, such as through a speaker,e.g., in a handset of the mobile computing device 750. Such sound caninclude sound from voice telephone calls, can include recorded sound(e.g., voice messages, music files, etc.) and can also include soundgenerated by applications operating on the mobile computing device 750.

The mobile computing device 750 can be implemented in a number ofdifferent forms, as shown in the figure. For example, it can beimplemented as a cellular telephone 780. It can also be implemented aspart of a smart-phone 782, personal digital assistant, or other similarmobile device.

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof.These various implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichcan be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms machine-readable medium andcomputer-readable medium refer to any computer program product,apparatus and/or device (e.g., magnetic discs, optical disks, memory,Programmable Logic Devices (PLDs)) used to provide machine instructionsand/or data to a programmable processor, including a machine-readablemedium that receives machine instructions as a machine-readable signal.The term machine-readable signal refers to any signal used to providemachine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device(e.g., a LCD (liquid crystal display) display screen for displayinginformation to the user and a keyboard and a pointing device (e.g., amouse or a trackball) by which the user can provide input to thecomputer. Other kinds of devices can be used to provide for interactionwith a user as well; for example, feedback provided to the user can beany form of sensory feedback (e.g., visual feedback, auditory feedback,or tactile feedback); and input from the user can be received in anyform, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in acomputing system that includes a back end component (e.g., as a dataserver), or that includes a middleware component (e.g., an applicationserver), or that includes a front end component (e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the systems and techniquesdescribed here), or any combination of such back end, middleware, orfront end components. The components of the system can be interconnectedby any form or medium of digital data communication (e.g., acommunication network). Examples of communication networks include alocal area network (LAN), a wide area network (WAN), and the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of thedisclosed technology or of what may be claimed, but rather asdescriptions of features that may be specific to particular embodimentsof particular disclosed technologies. Certain features that aredescribed in this specification in the context of separate embodimentscan also be implemented in combination in a single embodiment in part orin whole. Conversely, various features that are described in the contextof a single embodiment can also be implemented in multiple embodimentsseparately or in any suitable subcombination. Moreover, althoughfeatures may be described herein as acting in certain combinationsand/or initially claimed as such, one or more features from a claimedcombination can in some cases be excised from the combination, and theclaimed combination may be directed to a subcombination or variation ofa subcombination. Similarly, while operations may be described in aparticular order, this should not be understood as requiring that suchoperations be performed in the particular order or in sequential order,or that all operations be performed, to achieve desirable results.Particular embodiments of the subject matter have been described. Otherembodiments are within the scope of the following claims.

In various implementations, operations that are performed “in responseto” or “as a consequence of” another operation (e.g., a determination oran identification) are not performed if the prior operation isunsuccessful (e.g., if the determination was not performed). Operationsthat are performed “automatically” are operations that are performedwithout user intervention (e.g., intervening user input). Features inthis document that are described with conditional language may describeimplementations that are optional. In some examples, “transmitting” froma first device to a second device includes the first device placing datainto a network for receipt by the second device, but may not include thesecond device receiving the data. Conversely, “receiving” from a firstdevice may include receiving the data from a network, but may notinclude the first device transmitting the data.

“Determining” by a computing system can include the computing systemrequesting that another device perform the determination and supply theresults to the computing system. Moreover, “displaying” or “presenting”by a computing system can include the computing system sending data forcausing another device to display or present the referenced information.

The systems and techniques described here can be implemented in acomputing system that includes a back end component (e.g., as a dataserver), or that includes a middleware component (e.g., an applicationserver), or that includes a front end component (e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the systems and techniquesdescribed here), or any combination of such back end, middleware, orfront end components. The components of the system can be interconnectedby any form or medium of digital data communication (e.g., acommunication network). Examples of communication networks include alocal area network (“LAN”), a wide area network (“WAN”), peer-to-peernetworks (having ad-hoc or static members), grid computinginfrastructures, and the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

Further to the descriptions above, a user may be provided with controlsallowing the user to make an election as to both if and when systems,programs or features described herein may enable collection of userinformation (e.g., information about a user's social network, socialactions or activities, profession, a user's preferences, or a user'scurrent location), and if the user is sent content or communicationsfrom a server. In addition, certain data may be treated in one or moreways before it is stored or used, so that personally identifiableinformation is removed. For example, a user's identity may be treated sothat no personally identifiable information can be determined for theuser, or a user's geographic location may be generalized where locationinformation is obtained (such as to a city, ZIP code, or state level),so that a particular location of a user cannot be determined. Thus, theuser may have control over what information is collected about the user,how that information is used, and what information is provided to theuser.

FIG. 8 is an example user interface 802 showing a subject's brain ageand estimated future brain age. For example, the interface 802 may bedisplayed on the screen of a computing device 800, printed to a sheet ofpaper, and/or stored by computer memory. The interface 802 shows arecord of historic measures of brain age as has been described in thisdocument, though in some implementations, some of the brain age measuresmay come from the technology described in this document and somemeasures may be generated from other types of data gathering (e.g.,sleep session in a clinical environment).

The interface 802 can include a graphic with population trend lines806-814. For example, a trend line 810 can show the rate of brain ageincrease given chronological age increase for the population as a whole,or a specific subpopulation to which the subject is a member. Trendlines 808 and 812 can show trends that are greater and lower thanaverage, for example, +/−1 standard deviation or +/−10%. Trend lines 806and 814 can show trends that are greater and lower than average, forexample, +/−2 standard deviations or +/−25%.

Measured brain age values can be shown with elements 816-826, plottedagainst the trend lines 806-814. As can be seen, this subject has ahistory of brain age that is greater than the average, and for elements816-820, a trend of accelerating brain age compared to the referencepopulation.

Based on the record of brain ages used to generate the elements 816-826,predicted function-metric for the subject can be estimated to representa measure of predicted future physiological measures at a futurechronological. For example, these future measures of brain age can berendered as elements 828 and 830. In this example, the larger size anddifferent shading indicates to the viewer the relative confidence of theestimates, compared to the measured values for elements 816-826.

Records of one or more interventions can also be graphically shown withelements 832 and 834. With this arrangement, a viewer can advantageouslyperceive the impact of the interventions (e.g., that intervention 1 hadminimal impact on slowing brain age, but that intervention 2 was verysuccessful at reversing the subject's trend).

Referring to the figures, FIG. 9 shows an example system 900 fordetermining timing of electrophysiological events of a subject,consistent with embodiments of this disclosure. The system 900 caninclude a data acquisition device 901 that has one or more physiologicalsensors 904 and one or more stimuli generators 905. The system 900 caninclude a user interface 906, training computer hardware 910, operatingcomputing hardware 916, and a data source 918. The system 900 can beconfigured to collect data from one or more subjects 908, as will bedescribed in further detail below.

In some aspects, the data acquisition device 901 can be worn by thesubject 908 to collect data from the one or more physiological sensors904. For example, the data acquisition device 901 can be configured todetect, measure, monitor, and record brain activity usingelectroencephalography (EEG), eye activity using electrooculography(EOG), muscle activity using electromyography (EMG), cardiac activityusing electrocardiography (ECG), respiration rate (e.g., usingrespiratory inductance plethysmography (RIP), pressure sensor, and/or atemperature sensor), oxygen saturation (e.g., using pulse oximetry),heart rate (HR), blood flow, actigraphy during sleep, or any combinationthereof. The stimuli generators 905 can generate audio stimuli, opticalstimuli, visual stimuli, tactile stimuli, or combinations thereof to thesubject 908, and the physiological sensors 904 can collect data thatreflects the subject's 908 response to the stimuli.

The data collected by the data acquisition device 901 can becommunicated throughout the system 900. For example, the data from thedata acquisition device 901 can be displayed at the user interface 906,sent to the training computing hardware 910, sent to the operatingcomputing hardware 916, and sent to the data source 918. Each of thedata acquisition device 901, the user interface 906, the trainingcomputing hardware 910, and the operating computing hardware 916 canperform one or more of the processing steps described in further detailbelow (see e.g., FIGS. 12-14 ).

FIGS. 10A and 10B show an example of a data acquisition system 1000 thatincludes a data acquisition device 1001 on the head of a subject. Insome aspects, the data acquisition device 1001 can be the dataacquisition device 901 of FIG. 9 . The data acquisition device 1001 canbe a head-worn sensing device that includes one or more sensors and oneor more stimuli generators. The data acquisition device 1001 can have abody 1014 that can be a breathable material, for example a meshmaterial. The breathable material can allow the skin beneath the body1014 to breathe. The breathable material can be elastic and/orinelastic. The elastic properties and the curved shape of the body 1014can be configured to inhibit or prevent the data acquisition device 1001from slipping during use, such as when the user moves during sleep(e.g., when the user shifts position or when one of their limbs oranother person contacts the data acquisition device 1001). The body 1014can extend partially or completely around a perimeter of a head 1008 ofa subject.

The data acquisition device 1001 can be a removably attachable headband,cap, hat, strip (e.g., adhesive or hook-and-loop style fastening strip),biased band, or any combination thereof. The data acquisition device1001 can have a curved shape, and the shape can include a closed or openloop (e.g., annular or semi-annular shape). The data acquisition device1001 can extend partially or completely around a perimeter of the head1008. The data acquisition device 1001 can include the body 1014 thathas a curved profile that facilitates improved positioning of the dataacquisition device. For example, the curved shape of the body 1014facilitates a horizontal or nearly horizontal portion 1020 of the body1014 that is positioned at and extends across a forehead 1021 of thehead 1008 above the eyebrows of the subject. The body 1014 includes oneor more notches 1022 that are positioned such that the body 1014 extendsaround each ear 1023 of head 1008 with the notches 1022 aligned witheach ear 1023. The body 1014 further extends under a nape 1025 of theback of the head 1008. The curved shape of the body 1014 facilitatesproper positioning of the data acquisition device 1001 on the head 1008by inhibiting or preventing the data acquisition device 1001 fromslipping during use, such as when the user moves during sleep (e.g.,when the user shifts position or when one of their limbs or anotherperson contacts the data acquisition device 1001).

The body 1014 can include slip resistant edges that are configured tokeep one or more sensors of the data acquisition device 1001 in positionduring use such that there is strong contact and less resistance tomovement at the point where the sensors come into contact with the skin.This can advantageously ensure that the device sensors can have reliablecontact with the skin.

The data acquisition device 1001 can be configured to measure andcollect one or more physiological parameters during sleep. For example,the data acquisition device 1001 can be configured to detect, measure,monitor, and record brain activity, eye activity, muscle activity (e.g.,body posture, limb movements), cardiac activity (e.g., heart rate, heartrate variability (HRV)), respiration activity (e.g., respiration rate),blood oxygen saturation, blood flow rates, or any combination thereof.For example, the data acquisition device 1001 can be configured todetect, measure, monitor, and record brain activity usingelectroencephalography (EEG), eye activity using electrooculography(EOG), muscle activity using electromyography (EMG), cardiac activityusing electrocardiography (ECG), respiration rate (e.g., usingrespiratory inductance plethysmography (RIP), pressure sensor, and/or atemperature sensor), oxygen saturation (e.g., using pulse oximetry),heart rate (HR), blood flow, actigraphy during sleep, or any combinationthereof. The data acquisition device 1001 can be configured to detect,measure, monitor, and record pressure and temperature, for example,using one or more pressure sensors and/or one or more temperaturesensors. The data acquisition device 1001 can perform polysomnography(PSG) tests and can collect polysomnographic data. The data that iscollected is referred to throughout as acquired data, raw data, and/orsleep data.

As shown in FIGS. 10C and 10D, the data acquisition device 1001 can haveone or more data acquisition modules 1018 (also referred to aselectronics modules 1018), for example, 9 to 13 data acquisition modules1018, including every 1 module increment within this range (e.g., 2electronics modules). For example, the data acquisition device 1001 canhave one electronics module 1018. In another example, the dataacquisition device 1001 can have a plurality of data acquisition modules1018 spaced apart around the data acquisition device 1001 to providesensors at a variety of positions around the head 1008 of the subject tooptimize data collection.

The one or more data acquisition modules 1018 can be configured tomonitor and record one or more physiological activities during sleep.For example, the data acquisition modules 1018 can be configured todetect, measure, monitor, and record brain activity, eye activity,muscle activity, cardiac activity, respiration activity, blood oxygensaturation, blood flow, actigraphy, or any combination thereof (e.g.,using EEG, EOG, EMG, ECG, RIP, pulse oximetry, or any combinationthereof, respectively). The one or more data acquisition modules 1018can be computer interfaces, for example, brain computer interfaces(BCIs).

The data acquisition modules 1018 can have one or more electrodes,sensors (e.g., biosensors), accelerometers, or any combination thereof.For example, the data acquisition modules 1018 can have one or more EEGbiosensors, EOG biosensors, EMG biosensors, ECG biosensors, respirationrate biosensors, pulse oximetry biosensors, HRV biosensors, temperaturesensors, pressure sensors, or any combination thereof, including one ormore reference sensors and/or one or more ground electrodes.

The data acquisition modules 1018 can have a single-channel and/or amulti-channel EEG system. The multi-channel EEG system can be operatedas a single channel EEG system. The EEG system (single or multi-channel)can include one or more EEG sensors. The data acquisition device 1001(e.g., the data acquisition modules 1018) can have 1 to 10 EEG sensors,including every 1 EEG sensor within this range (e.g., 4 EEG electrodes).The data acquisition modules 1018 can have more than 10 sensors (e.g., 1to 100 EEG sensors). The data acquisition modules 1018 can have an EEGsensor array or an EEG sensor network (e.g., of 2 to 10 or moresensors). One of the EEG sensors can be a ground electrode. The EEGsystem can have one or multiple reference electrodes (e.g., one or tworeference electrodes). The electronics module 1018 can have, forexample, three channels of frontal EEG and one EEG reference sensor orthree channels of prefrontal EEG and one EEG reference sensor. The EEGelectrodes can be positioned on the forehead, for example, the EEGelectrodes can be placed at forehead positions such as Fp1 and Fp2. TheEEG electrodes can be placed according to the international 10-20system.

The data acquisition modules 1018 can have 2, 3, or 4 EOG sensors. TwoEOG sensors can detect/measure movement of one or both eyes. Forexample, two EOG sensors can be positioned to detect/measure eyemovement of the left and right eyes (e.g., a first EOG sensor can bepositioned on the right outer edge of the right eye and a second EOGsensor can be positioned on the left outer edge of the left eye), twoEOG sensors can be positioned to detect/measure eye movement of only theleft eye (e.g., a first EOG sensor can be positioned on the right outeredge and a second EOG sensor can be positioned on the left outer edge ofthe left eye), or two EOG sensors can be positioned to detect/measureeye movement of only the right eye (e.g., a first EOG sensor can bepositioned on the right outer edge and a second EOG sensor can bepositioned on the left outer edge of the right eye). Three EOG sensorscan be positioned to detect/measure eye movement of the left and righteyes (e.g., a first EOG sensor can be positioned on the right outer edgeof the right eye, a second EOG sensor can be positioned on the leftouter edge of the left eye, and a third EOG sensor can be positionedbetween the left and right eyes). The three EOG sensors can selectivelydetect/measure eye movement of the left and/or right eyes, with thefirst and third EOG sensors configured to detect/measure movement of theright eye, with the second and third EOG sensors configured todetect/measure movement of the left eye, and with the first and secondEOG sensors configured to detect/measure movement of the left and righteyes together. Four EOG sensors can be positioned to detect/measure eyemovement of the left and right eyes (e.g., first and second EOG sensorscan be positioned on first and second sides of the left eye and thirdand fourth EOG sensors can be positioned on first and second sides ofthe right eye). The “outer edges” of the eyes can be in line with theeyes, above the eyes and/or below the eyes.

The data acquisition system 1000 can have 1 to 6 EMG sensors, includingevery 1 EMG electrode increment within this range (e.g., 2 EMGelectrodes). The data acquisition system 1000 (e.g., the dataacquisition device 1001 and/or the data acquisition modules 1018) canhave 1 to 10 ECG sensors, including every 1 ECG electrode incrementwithin this range (e.g., 1, 2, or 3 ECG electrodes). The ECG sensors canbe used to measure HRV. The ECG sensors can be used to determine HRV.

The data acquisition system 1000 (e.g., the data acquisition device 1001and/or the data acquisition modules 1018) can have 1 to 10 heart ratesensors (e.g., photoplethysmography (PPG) sensor), including every 1heart rate sensor increment within this range (e.g., 1, 2, or 3 heartrate sensors). The heart rate sensors can be used to measure HRV. Theheart rate sensors can be used to determine HRV.

The data acquisition system 1000 (e.g., the data acquisition device 1001and/or the data acquisition modules 1018) can have one or multiplepressure sensors (e.g., pressure transducers) and/or temperature sensors(e.g., thermocouples) configured to monitor respiration. For example,the data acquisition device 1001 can have 1 to 4 pressure sensors,including every 1 pressure sensor increment within this range (e.g., 1or 2 pressure sensors). The data acquisition device 1001 can have 1 to 4temperature sensors, including every 1 temperature sensor incrementwithin this range (e.g., 1 or 2 temperature sensors). The pressureand/or temperature sensors can be positionable near the nostrils and canbe configured to detect the pressure/temperature changes that occur whena user inhales and exhales. The pressure and/or temperature sensors canbe attached to or integrated with the data acquisition device 1001 suchthat when the data acquisition device 1001 is removably secured to ahead, the pressure and/or temperature sensors are positioned in abreathing flow path (e.g., near the nostrils and/or mouth, for example,for mouth breathers).

The data acquisition device 1001 can have a pulse oximetry sensor thatcan be removably attachable to an ear, for example, to an ear lobe. Thedata acquisition system 1000 can have a pulse oximetry sensor that canbe removably attachable to a finger. The finger pulse oximetry sensorcan be in wired or wireless communication with the data acquisitiondevice 1001 (e.g., to the electronics module 1018) and/or to the datadisplay device. The ear pulse oximetry sensor can be attached to orintegrated with the data acquisition device 1001. The pulse oximetrysensor (ear and finger sensor) can be a component of a clip. The clipcan attach to (e.g., clip to) an ear lobe or a finger. The clip can beattached to or integrated with the data acquisition device 1001, forexample, to the body 1014. The pulse oximetry sensor can be placed onthe forehead. The forehead pulse oximetry can be attached to orintegrated in the data acquisition device 1001.

The data acquisition device 1001 can have one or more pressure sensors(e.g., 1, 2, 3, 4, 5, 6 or more) configured to detect when the dataacquisition device 1001 is attached to a head, for example, by measuringthe amount of force exerted against each of the pressure sensors. Thedata acquisition system 1000 can be configured to detect whether thedata acquisition device 1001 is properly positioned on the head, forexample, by detecting and/or comparing the different pressures measuredby the one or more pressure sensors (e.g., by calculating one or morepressure differentials). The pressure sensors can also be used todetermine whether the position can be improved or further optimized, forexample, for more accurate and/or reliable data collection. The dataacquisition device 1001 can be activated (e.g., automatically ormanually) when positioned on the head 1008 as a result of one or morepressure sensors exceeding a pressure threshold. The data acquisitiondevice 1001 can be activated (e.g., automatically or manually) whenpositioned on the head 1008 as a result of one or more differentialpressure differentials (e.g., between two sensors) falling below adifferential pressure threshold.

For example, a first pressure sensor can be on a first side of the dataacquisition device 1001 and a second pressure sensor can be on a secondside of the data acquisition device 1001. The pressure sensors can beseparated by about 9 degree to about 980 degrees as measured from acenter of the data acquisition device 1001 (e.g., along a longitudinaland/or transverse axis), including every 1 degree increment within thisrange. The center of the data acquisition device 1001 can fall betweentwo inner sides of the device such that the device center is not on thebody and/or edges of the data acquisition device 1001. A 180 degreeseparation can correspond to a configuration in which the first andsecond pressure sensors are diametrically opposed from one another.Angles less than 180 degrees can correspond to configurations in whichthe first and second pressure sensors are on opposite sides of thedevice, determined for example relative to a reference axis. Angles lessthan 180 degrees can correspond to configurations in which the first andsecond pressure sensors are on the same side of the device, determinedfor example relative to a reference axis. The first and second pressuresensors can be used to determine a side-to-side or a front-to-backpressure differential of the data acquisition device 1001 (i.e., thepressure levels on the left side, right side, front side, and/or backside of the data acquisition device 1001). Four pressure sensors can beused to determine side-to-side and/or front-to-back pressuredifferentials of the device when removably attached to a head. Theangles between sensors can be from about 1 degree to about 180 degrees,including every 1 degree increment within this range.

The data acquisition system 1000 (e.g., the data acquisition device1001, a user's device, and/or a remote server) can determine whether thedata acquisition device 1001 is properly or improperly positioned byanalyzing the pressure readings of the one or more pressure sensors. Thedata acquisition system 1000 can assess the quality of the data signalsfrom the data acquisition modules 1018 to ensure proper stability andcontact of data acquisition modules 1018 is occurring to ensure highquality data is being obtained by the data acquisition device 1001. Ifproperly positioned, the data acquisition device 1001 can automaticallybegin collecting data (e.g., immediately or after one or more additionalconditions are satisfied). The data acquisition device 1001 can collectdata when not positioned properly, however, some of the data may haveaccuracy, precision and/or reliability issues, or some of the data maybe missing altogether (e.g., pulse oximetry data). The data acquisitionsystem 1000 can notify the user that the data acquisition device 1001 isnot positioned properly. Additionally or alternatively, the dataacquisition system 1000 can be configured to determine whether the dataacquisition device 1001 is properly positioned by measuring the voltagedrop across one or more sensors of the data acquisition modules 1018).

The data acquisition device 1001 can begin collecting data when one ormore conditions are satisfied (e.g., 1 to 10 or more conditions). Thedata acquisition device 1001 can begin collecting data when a properposition is detected. The data acquisition device 1001 can begincollecting data when the data acquisition system 1000 detects that theuser is in a sleeping position and/or when the user is in a sleepinglocation, for example, for a predetermined amount of time (e.g.,immediately (no time), or after 1 min to 5 min or more have elapsed).The sleeping location can be established or otherwise settable by theuser. For example, the data acquisition device 1001 can begin collectingdata after first, second, third, and/or fourth conditions are satisfied.The data acquisition device 1001 can begin collecting data immediatelyafter any one condition or combination of conditions is satisfied. Thefirst condition can correspond to correct device placement (e.g., of thedata acquisition device 1001). The second condition can correspond touser input (e.g., selection of a command prompt). The third conditioncan correspond to a position of the device relative to the environment,for example, whether the orientation of the data acquisition device 1001is in a position indicative of a sleeping position of the user (e.g.,lying down, either prone, supine, or on side). The fourth condition cancorrespond to a location of the user (e.g., on a bed). Sleep datacollection can begin when the pressure sensors detect that the dataacquisition device 1001 is properly attached to a head, when the dataacquisition modules 1018 begin collecting data, or when the acquireddata meets quality thresholds.

The data acquisition device 1001 can have one or more temperaturesensors (e.g., 1, 2, 3, 4 or more temperature sensors) configured tomonitor a user's body temperature. The temperature sensors can betemperature transducers (e.g., thermocouples). The temperature sensorcan be attached to or integrated with the data acquisition device 1001.The temperature sensors can be configured to detect when the dataacquisition device 1001 is attached to a head, for example, by detectinga body temperature. An environment temperature sensor can be configuredto measure environmental temperature. The environment temperature sensorcan be one of the temperature sensors of the data acquisition device1001. The environment temperature sensor can be a temperature sensor ofa sleeping location (e.g., house or apartment). The data acquisitionsystem 1000 can determine a user's optimum sleeping temperature andsuggest a sleeping temperature for the user, for example, from about 60degrees Fahrenheit to about 85 degrees Fahrenheit, including every 1degree increment within this range.

The data acquisition system 1000 (e.g., the data acquisition device 1001and/or the data acquisition modules 1018) can have one or moreaccelerometers (e.g., one accelerometer). The accelerometer can beattached to the data acquisition device 1001 or can be wirelesslyconnected (e.g., located at the subject's wrist, finger, or otherlocation). In some aspects, the accelerometer can detect limb movementsof the subject. The accelerometer can detect a user's positional state,for example, a user's movement or sleeping pose (e.g., prone, on side).The accelerometer can be a two-axis accelerometer. The accelerometer canbe a three-axis accelerometer. The accelerometer can be configured todetect head, body, and/or limb movements, or any combination thereof.The accelerometer can be used to detect lack of movement as well, forexample, the length of time in a single position without movement orwith movement within a specified tolerance (e.g., voltage level ormovement amount, for example, 5 cm or less).

The electronics modules (e.g., data acquisition modules 1018) caninclude, for example, three channels of prefrontal EEG and one EEGreference sensor to detect brain wave activity, a heart rate sensor(e.g., a pulse oximetry sensor, an ECG sensor, or other sensorsdescribed throughout) to monitor cardiac activity (e.g., RRvariability), an accelerometer (e.g., two or three axis accelerometer)to detect head, body, and/or limb movements, or any combination thereof.

The electronics module 1018 can be configured to contact a user's skin(e.g., a user's forehead) during use. The data acquisition device 1001can press the EEG sensors and/or ECG sensor(s) against the user's skin(e.g., forehead) when secured to the head 1008, for example, with anelastic fit or with an interference fit. Alternatively or additionally,the sensors can be adhered to the user's skin (e.g., forehead) using anadhesive with or without the data acquisition device 1001.

The electronics module 1018 can be configured to measure brain activity,for example, during light sleep, during rapid eye movement (REM) sleep,during slow-wave sleep (SWS) (also referred to as deep sleep), or anycombination thereof. The electronics module 1018 can be configured tomeasure cardiac activity, for example, HRV such as RR intervals. Theelectronics module 1018 can be configured to detect a user's motionand/or a user's lack of motion. These sensors may be integral to othercomponents (e.g., stitched into a headband), may be removable (e.g., byremovable snap or friction fit), or may be otherwise used with headbandsor other worn devices.

The electronics module components (e.g., channels, sensors,accelerometers, stimuli generators) can be attached to or integratedwith the data acquisition module 1018. The data acquisition module 1018can be permanently attached to, removably attached to, or integratedwith the data acquisition device 1001 (e.g., to and/or with the body1014). Additionally or alternatively, the various activity-measuringcomponents (e.g., channels, sensors, accelerometers) can be attached toor integrated with an attachment portion of the data acquisition device1001, for example the body 1014 separate and apart from the module 1018.The module 1018 can be interchangeable with one or more other modules(not shown) having a different number of sensors, one or more differenttypes of sensors, or otherwise having at least one differentparameter-measuring capability relative to the electronics module 1018.The electronics module 1018 can be interchangeable with another modulehaving the same exact module or otherwise with another module having thesame exact parameter-measuring capabilities. Different electronicsmodules 1018 can have different sizes relative to one another. Differentmodules 1018 can have different shapes relative to one another.

The data acquisition device 1001, a user device, and/or a remote servercan analyze the sleep data collected, as described in further detailbelow. The data acquisition device 1001, the user device, and/or aremote server can determine one or more parameters from the datacollected, for example, using one or more programmable processors. Theparameters can include total light sleep, total SWS (also referred to astotal deep sleep), total REM sleep, total non-REM sleep (total lightsleep and total SWS added together), total sleep (total REM and non-REMsleep added together), longest deep sleep duration, deep sleepamplitude, strength of deep sleep, heart rate, heart rate variability,total time in bed, time to fall asleep, time awake between fallingasleep and waking up, various sleep microstructure features (e.g.,number of sleep slow oscillation (SO) events described in further detailbelow), or any combination thereof. The time-based parameters (e.g., the“total,” “duration,” and “time” parameters) can be measured in the timedomain, for example, using seconds, minutes, hours. Days, weeks andyears can be used for accumulated and/or running totals.

The total time in bed parameter can be measured from a start point to anend point. The start point can correspond to when the user manuallyactivates the data acquisition device 1001, for example, by selecting astart instruction (e.g., “ready to sleep”) on the display 102. The startpoint can correspond to when the data acquisition device 1001 isactivated (e.g., automatically or manually). The data acquisition device1001 can be automatically activated, for example, when a voltage isdetected across two or more sensors of the module 1018 (e.g., across twoor more of the EEG electrodes). The voltage can indicate contact withskin and cause the data acquisition device 1001 to begin measuring thetotal time in bed. The data acquisition device 1001 can have a timer.The data acquisition device 1001 can be automatically activated whenpositioned on the head 1008 as a result of one or more pressure sensorsexceeding a pressure threshold. The end point can correspond to when theuser manually deactivates the data acquisition device 1001, for example,by selecting an end instruction (e.g., “turn off alarm” or “get sleepreport”) on the display 102. The end point can correspond to when thedevice is automatically deactivated. The data acquisition device 1001can be automatically deactivated, for example, when the accelerometerindicates the user is walking around or has taken the data acquisitiondevice 1001 off their head.

The data acquisition system 1000 can provide audio stimulation (alsoreferred to as audio entrainment) using, for example, one or more soundwave generators 1017 (e.g., 1 to 4 sound wave generators). The soundwave generators can be, for example, speakers. A portion of the dataacquisition device 1001 can be positionable over and/or engageable witha left and/or right ear of a user such that the sound wave generators1017 can emit sound into a user's ears. The sound wave generators 1017can be attached to, embedded in, or integrated with the device body1014. The sound wave generators 1017 can be in wired or wirelesscommunication with the data acquisition device 1001, a user device, aremote server, or any combination thereof. The sound wave generators1017 can be micro speakers. The sound wave generators 1017 can be inwired or wireless communication with the data acquisition device 1001and can be attached

Additionally or alternatively, the data acquisition system 1000 canprovide audio stimulation via bone conduction by transmitting soundsignals through bone to a user's inner ear. The data acquisition system1000 can have one or more actuator assemblies 1013 to provide boneconduction sound transmission. The actuator assemblies 1013 can have anactuator (e.g., a transducer). The actuator can be vibratable (e.g., theactuator can be configured to vibrate). The actuator assemblies 1013 canhave a transceiver coupled to the actuator. The transceiver can causethe actuator to vibrate to generate sound, for example, when thetransceiver is electronically driven with sound signals (e.g., from adriver and/or a controller, for example, from the data acquisitiondevice 1001). The actuator can be a piezoelectric actuator. Thepiezoelectric actuator can be configured to move a mass to provide soundthrough bone. The actuator assemblies 1013 (e.g., the actuator) can bepositioned near the ear and/or on the cheek. For example, the actuatorassemblies 1013 can be positioned on a user's skin proximate thezygomatic bone, the zygomatic arch, the mastoid process, or anycombination thereof. The data acquisition system 1000 can have 9 to 14actuator assemblies, or 9 to 14 actuators, including every 1 actuatorassembly/actuator increment within these ranges.

The data acquisition system 1000 can provide visual/optical stimulation(also referred to as light entrainment) using, for example, one or morelight emitting sources 1019 (e.g., 9 to 100 light emitting sources). Aportion of the data acquisition device 1001 can be positionable overand/or engageable with a left and/or right eye of a user such that thelight sources 1019 can emit light into a user's eyes (e.g., through theuser's closed eyelids). The data acquisition device 1001 can beconfigured to partially or completely cover one, both, or no eyes. Thedata acquisition device 1001 can be configured for temporary securementabove or proximate to a user's eyes/eyelids. For example, a portion ofthe data acquisition device 1001 can be configured to rest againstand/or adhere to an eyebrow, the area proximate an eyebrow, theglabella, the nose (e.g., dorsal bridge, dorsal base, tip), cheek, orany combination thereof. The light sources 1019 can be attached to,embedded in, or integrated with the data acquisition device 1001.

The data acquisition system 1000 can provide audio entrainment, opticalentrainment, cranial electrotherapy stimulation (CES), or anycombination thereof, in addition to or in lieu of the data collectionand associated analyses described below.

FIG. 11A shows an example system 1100 for determining metrics of asubject, such as SO timing data for a user of a head-worn sensing device(e.g., data acquisition device 1001) as previously described. In thissystem, one or more training subjects 1102 provide training data throughphysiological sensors 1104 and user interfaces 1106 (either directly orby another user such as an administrator or health-care provider), whichcan be combined with tagging data 1108 by training computing hardware1110 to generate one or more function-metric classifiers 1112. Operatingsubjects 1114 can then use operating computing hardware 1116 to collectdata through physiological sensors 1118 and/or user interfaces 1120 togenerate one or more function metrics 1122. The data provided throughthe user interface 1106 can include information about the subject 1102useful for tagging data collected from the subject using thephysiological sensors 1104. For example, name, age, and otherdemographic data may be used to create subpopulation samples of senseddata. Instructions can be presented to the subject 1102 to don thephysiological sensors 1104 and, upon pressing an interface element(e.g., a button on a screen) confirming the sensors 1104 are in place,sensing can begin.

Training subjects 1102 are a group of subjects (e.g., human or otheranimals) that contribute data to be used as training data. For example,the subjects 1102 may be patients who, under a program of informedconsent, provide some of their medical records for research purposes. Inanother example, the subjects 1102 may be generally healthyrepresentatives of a population that have agreed to contribute trainingdata. The training subjects 1102 may be organized by physiological(e.g., healthy vs having a known medical issue, menopausal status,menstrual cycle phase, sleep disorders, hypertension, diabetes, mentalor behavioural health condition), demographic details (e.g., age,gender, geographic location, location of residence, education level,professional level), and/or groups based on lifestyle factors (e.g.,activity level, past or current behaviours, schedule such as shift work,short sleepers). Thus, classifiers 1112 may be created for thepopulation as a whole, or for particular subpopulations (e.g.,stratified by health status, age, or other factors expected to impactthe operation of the classifiers). In some cases, each classifier 1112may be personalized, using a single subject 1102 to create or modify aclassifier, where the training subject 1102 is also the operatingsubject 1114 so that their personal classifier is used later inoperation.

Physiological sensors 1104 include one or more sensors that can senseone or more physiological phenomena of the subjects 1102. In some cases,the sensors 1104 can include sensors mounted in a head-worn device suchas the data acquisition modules 1018 of the data acquisition device 1001and the physiological sensors 904 of the data acquisition device 901.However, other arrangements are possible such as bespoke trainingsensors used only for the collection of training data, or use of datacollected with other sensors for other purposes (e.g., use of some of,but not all, data generated in clinical sleep studies).

The user interface 1106 can include hardware and corresponding softwareto present user interfaces to a user (e.g., subject 1102 or anotheruser) to collect data about the subject 1106. This can include thedemographic data described, can present information to the subject 1102about the use of data collected and aid in the development of informedconsent, etc. The user interface 1106 can include a personal computingdevice such as a desktop or laptop, a mobile computing device such as aphone, tablet, raspberry pi or other appropriate elements for user inputand output.

Tagging data 1108 includes data that annotates data from thephysiological sensors 1104 and/or the user interface 1106. For example,a user (shown or not shown) and/or an automated system (shown or notshown) can annotate data from the physiological sensors 1104 to mark thesubject 1102 as in states such as sleep-states, and to mark the datarelated to SOs (e.g., SO peak timing, SO peak values, SO-spindlecoupling). These tags in the tagging data 1108 can also include otherdata that can be used in the creation of the classifiers.

The training computer hardware 1110 can receive the tagging data 1108,data from the physiological sensors 1104, and/or data from the userinterface 1106 to generate one or more SO classifiers 1112. Exampleprocesses for such classifier creation are described in greater detailelsewhere in this document. The classifier 1112 can, given a particularset of inputs, generate one or more predictions of SO value. These SOvalue predictions can include values that give an indication of theexpected state of EEG signals (or other signals) of training subjects1102 while they are being monitored with the physiological sensors 1104.

These SO value predictions can be generated for the subject 1114 withinthe same sleep session, and even within the same single wave. FIG. 11Bshows an example of a single wave of the subject 1114, annotated with anumber of possible event points. In an example, a SO classifier 1112 maybe provided with EEG data for wave from time 0 milliseconds to time 50milliseconds (i.e., at point steady1), and the classifier 1112 canpredict the timing of zx2 at 150 milliseconds. Importantly, theoperation of the classifier can be completed in less than 100milliseconds, meaning the prediction of the timing of zx2 is generatedbefore the subject actually experiences activity of zx2, allowing forthe timing of stimulation at xz2. For a system with 17 milliseconds oflead time between instructing actuation of stimulation and performanceof the stimulation, the actuation instruction can be issued at time 133milliseconds, resulting in delivery of the stimulus at time 150milliseconds—the predicted time of zx2. This timing can use a data valuefor a decision point (e.g., a point a given number of milliseconds fromthe start of an SO waveform, a point at or referenced to a particularpoint in the waveform). This point of decision can be used as the lastpoint in time at which an instruction can be issued while stillaccounting for all delays, including filtering delays, actuation delays,etc.

Returning to FIG. 11A, after the classifier 1112 has been created, oneor more operating subjects 1114 can use the physiological sensors 1118and/or user interface 1106 to provide new data to the operatingcomputing hardware 1116. The computing hardware can use this new datawith the classifier 1112 to create new SO classifiers 1112 for theoperating subjects 1114. Said another way, the users 1114 can wear aheadband (e.g., data acquisition device 1001) to bed as previouslydescribed, and they can receive stimulus as previously described. Inaddition to tracking the subject's 1114 EEG for delivery of thestimulus, the hardware 1116 can also refine the classifiers 1112 withthe tracked EEG of the subject 1114. As will be appreciated, this canadvantageously provide the users 1114 with a system that both i)improves their health or wellness, resulting in a beneficial change inneurophysiological behavior and ii) updates for the changingneurophysiological behavior. The users 1114 can also be provided withassessments or reports of their changing neurophysiological behavior.

FIG. 11B shows a subject brain 1150. Schematically shown is brainactivity 1152, for example the subject sleeps. As will be understood,brain activity generally includes electrochemical or other processeswithin the subject brain, and the depiction 1152 is a schematicrepresentation of these activities shown for illustrative purposes. EEGdata 1154 represents EEG readings and/or other data streams generatedfrom sensing of the activity of the brain 1150. As will be appreciated,in the depicted data a current time is shown at time 1156. Earlieractivity is shown farther to the left of the current time 1156. To theright of current time 1156, no activity 1152 or EEG data 1154 is shown,as the activity has not occurred yet.

Individual SO waveforms 1158, 1160, and 1162 can be identified inreal-time. That is to say, as the waveform 1158 was being sensed in theactivity 1152, it may be identified. Then, when waveform 1160 was beingsensed in the activity 1152, it may be identified. Now, at the currenttime 1156, the current SO waveform 1162 may be sensed, even as it is notyet completed.

Callout box 1164 shows the current SO waveform 1162 in greater detail.As will be appreciated, a current SO record can be stored in computermemory to record data for the current SO waveform 1162. In real-time,concurrently with the sensing and identification of the current SOwaveform 1162. Various fiducial points before the current time areidentified in real time (zx1, steady1, neg_time, neg_val, zx2, andsteady2 in this example). Predictions for electrophysiological points orfiducial points that have not yet occurred (e.g., predicted_pos_time=225milliseconds for pos_time and predicted_pos_val=43 μV for pos_val), canbe generated before those electrophysiological events or fiducial pointsof the waveform occur in the activity 1152. In this way, an expectedtime, voltage, or other value can be predicated and stored in computermemory before the corresponding brain activity 1152 occurs. This canadvantageously allow for the timing of automated events such as thedelivery of subject stimulation targeted at a particular portion of SO.

Features may be generated from the fiducial points. For example,measures in time between two fiducial points may be used as a feature.In addition or in the alternative, other types of features may be used.A feature may be a measure of magnitude at a fiducial point. Somefeatures are in different domains. For example, a frequency domainfeature may include a measure related to frequency. For example, aspindle domain feature may include measures related to spindle coupling,timing of spindle maximum, the maximum value itself, etc.

FIG. 12 shows an example process that can be used to produce classifiersable to evaluate subject data (e.g., sleep data) and generate stimulusto a subject based on determined timings of electrophysiological eventsof the subject. For example, the process 1200 can be performed by theelements of the system 1100 and will be described with reference tothose elements. However, other systems can be used to perform theprocess 1200 or similar processes.

Generally speaking, the process 1200 includes data collection 1202-404,feature engineering 1206-410, and machine learning training 1212-414. Inthe data collection 1202-404, data is gathered in formats in which it isgenerated or transmitted, then reformatted, decorated, aggregated, orotherwise processed for use. In the feature engineering 1206-410, datais analyzed to find those portions of the data that are sufficientlypredictive of a physiological function (e.g., brain function) to be usedto train the classifiers. This can allow the discarding of extraneous orunneeded data, improving computational efficiency and/or accuracy. Themachine learning training 1212-414 can then use those features to buildone or more models that characterize relationships in the data for usein future classifications.

In the data acquisition 1202 for example, the computing hardware 1110can collect data from the sensors 1104, from the user interface 1106,and the tagging data 1108. As will be understood, this acquisition mayhappen over various lengths of time and some data may be collected afterother data is collected.

In the preprocessing and classifying 1204 for example, the computinghardware 1110 can perform operations to change the format orrepresentation of the data. In some cases, this may not change theunderlying data (e.g., changing integers to equivalent floating pointnumbers, marking epochs of time in time-series data), may destroy someunderlying data (e.g., reducing the length of binary strings used torepresent floating point numbers, applying filters to time-series data),and/or may generate new data (e.g., averaging two prefrontal EEGchannels such as Fp1 and Fp2 to create a single, virtual prefrontal EEGsignal; mapping annotations to the data).

In the feature extraction 1206 for example, the computing hardware 1110can extract features from, e.g., fiducial points recorded in theprocessed and classified data. Some of these features can be related toSOs of EEG signals. For example, individual waves can be isolated,tagged with timing data, and various features of wave morphology can beannotated. These waves can be filtered (e.g., removing anomalous waves)and aggregated into representative samples (e.g., by taking a weightedor unweighted average of various values), with one such example shown inFIG. 11B. Fiducial points can be identified according to heuristic rulesapplied to the morphology or local shape of various points of the wavedata. For example, a rule for generating a neg_time tag may be toidentify a minimum value in the wave as neg_time.

One example scheme of fiducial points is as follows. Various other SOfiducial points (or features) can also be defined, e.g. the points atwhich EEG signal value exceeds a defined percentage of the SO negativepeak amplitude, either in the negative or the positive direction.

Zx1 can be defined as timing of the first positive-to-negativezero-crossing in the real-time filtered EEG signal, for a given SO

Steady1 can be defined as timing of the point after zx1 at which the EEGsignal slope exceeds in negativity a predefined negative slope threshold

Neg_time can be defined as timing of the SO negative peak

Neg_val can be defined as the amplitude of the SO negative peak

Steady2 can be defined as timing of the point before zx2 at which theEEG signal slope exceeds in positivity a predefined positive slopethreshold

Zx2 can be defined as timing of the first negative-to-positivezero-crossing in the real-time filtered EEG signal, for a given SO

Features 1-7 in FIG. 11B can be used to record the time (e.g., inmilliseconds), or the difference in EEG amplitude (e.g., in microvolts)between various fiducial points and features in the wave as shown.

In the feature transformation 1208 for example, the computing hardware1116 can modify the features in ways that preserve all data, destroysome data, and/or generate new data. For example, values may be mappedto a given scale (e.g., mapped to a log scale, mapped to scale of 0 to1). In some cases, statistical aggregates can be created (e.g., meanvalues, standard variation). These aggregates may be generated from datafor each sleep session, across the detected sleep stages, or mayaggregate data across multiple sleep sessions.

In the feature selection 1210 for example, the computing hardware 1116can select some of the features for use in training the model. This caninclude selecting a proper subset (e.g., some, but not all) of thefeatures.

In particular, some wave data can be tagged for use in training, andsome wave data can be tagged for exclusion from training. In someinstances, some waves can exhibit morphology consistent with ‘typical’or ‘normal’ brain activity while some waves can exhibit morphology thatis not consistent with ‘typical’ or ‘normal’ brain activity. These wavesare sometimes referred to as “well-behaved” and “poorly-behaved”. Assuch, waves can be tagged with an inclusion/exclusion tag to designateif the wave should be used or excluded from model training and/or modeldeployment (e.g., preventing stimulation when such a SO wave isobserved).

As will be appreciated, “poorly-behaved” waves may be the product ofnoisy data collection by sensors. Additionally or alternatively, the“poorly-behaved” waves may be the product of accurate sensing ofatypical—but potentially normal and healthy—brain activity that does notconform to typical SO wave morphology or patterns. Regardless of thecause, these “poorly-behaved” waves tagged for exclusion can be handledas having low predictive value and excluded from model training, whilethe “well-behaved” waves tagged for inclusion can be handled as havinghigh predictive value and included in model training.

In the model training 1212 for example, the computing hardware 1116 cantrain one or more machine-learning models using the selected features(e.g., wave data marked for inclusion and excluding wave data marked forexclusion). In some cases, one or more models are created that proposemappings between the features and tagged data indicating timing offeatures (e.g., in milliseconds from the onset of a particular wave) forthose features. Then, the computing hardware 1116 modifies thosemappings to improve the model's accuracy.

In the output evaluation 1214 for example, the computing hardware 1116can generate one or more functions, sometimes called classifiers, whichinclude a model. This inclusion can involve including the whole model,or may involve only including instructions generated from the modelallowing for the classifier to have a smaller memory footprint than themodel itself.

Described now will be one example implementation of the process 1200.While a particular number, type, and order of details are selected forthis implementation, it will be understood that other numbers, types,and order of details may be used to implement the process 1200 or otherprocesses that accomplish the same goals.

In the data acquisition 1202 in this implementation, basic informationabout a subject can be acquired such as one or more of a subject's: nameor identification (ID), age, gender, other sociodemographic data,health-related information, physiological information (e.g., menopausalstatus and menstrual cycle information), and information related tolifestyle or behaviors (including but not limited to sleep habits,tobacco/alcohol consumption, exercise, meditation, among others). Thisdata can be user inputted or integrated from other devices.

The system(s) described above can enable the real-time open-loop orclosed-loop delivery of stimuli, which include at least one or more ofaudio, light, vibratory, or electrical stimuli, as part of the dataacquisition procedure.

The subject's raw biological information can be collected, which caninclude of at least one or more sleep recordings where each recordingincludes at least one prefrontal EEG channel and can include additionalsensors such as: additional EEG channels, forehead photoplethysmogram(PPG), blood oxygen saturation (SpO₂), EMG, EOG, electrodermal activity(EDA), and actigraphy (movement) sensors. In some cases, systems can usetwo prefrontal EEG channels (Fp1 and Fp2), with or without PPG, SpO₂,and actigraphy. The data can be collected from full night recordingsand/or less-than full nights (e.g., naps).

The output of the data acquisition 1202 can include each subject's rawbiological signals and stimulus types and timings, from one or multiplerecordings, as well as the subject's age and other basic information.

In the preprocessing and classifying 1204 in this implementation,preprocessing and automated sleep-stage classification can includevarious operations that may be performed on the output of the dataacquisition 1202. For example, two bandpass-filtered (0.2-40 Hz)prefrontal EEG signals are averaged to obtain a single virtualprefrontal EEG channel. Heartbeat times are extracted from the filteredand demodulated PPG signal using peak detection.

The data is segmented into discrete overlapping and/or non-overlappingepochs and each epoch is described using a set of time-domain,frequency-domain and other EEG and HRV features typically used for sleepstage classification. Each epoch is classified as either wakefulness(W), rapid eye movement (REM) sleep, non-REM sleep stage 1 (N1), non-REMsleep stage 2 (N2), or deep sleep (N3), using an automatic sleep stageclassification algorithm based on machine learning (ML) and theextracted sleep EEG features.

Each epoch can be annotated as containing a SO wave, containing thebeginning of such a wave (e.g., containing zx1), and/or containing otherfiducial points of a wave. This annotation may in some cases beautomated for example with offline (e.g., after all data is gathered andafter the sleep session has ended) analysis or online (e.g., during thesleep session, for past SOs) analysis. This annotation may in some casesbe manual, with a technician reviewing and tagging the data. Thisannotation may in some cases be automated, with a computational analysisbeing performed using a pre-defined ruleset to annotate the data. Thisannotation may in some cases be a mix of manual and automatedoperations.

The output of the preprocessing and classifying 1204 can include eachsubject's preprocessed biological data, from one or multiple recordings,segmented into time-based epochs. For example, the time-based epochs canbe segmented into epochs corresponding to the length of a single SO wave(e.g., 250 to 300 milliseconds in one example).

In the feature extraction 1206 in this implementation, SO wave featuresare computed. These features may be recorded, for example, in a count ofmilliseconds from the onset of the given wave, in distances (in time)between fiducial points, in amplitudes or maximum or minimum values,etc.

The output of the feature extraction 1206 can include each of thesubject's recordings, either one or multiple, described with an array ofwave features, as well as EEG-based, HRV-based and SpO₂-based sleepmicrostructure features.

In the feature transformation 1208 in this implementation, the computedfeatures are transformed using lossy or lossless data compression foradvantageously efficient storage and transmission.

In the feature selection 1210 data for various SO waves of variousdemographics and SO wave types are examined to be tagged for inclusionor exclusion in model training. In some examples, a reference wave isdefined which exhibits morphology of SO waves identified as “typical” orotherwise highly predictive. A difference value for each candidate waveis then calculated to represent a measure of difference between thecandidate wave and the reference wave. Candidate waves with a differencevalue less than a threshold value can be marked for inclusion, whilewaves with a difference value greater than the threshold value can bemarked for exclusion. In some cases, candidate waves can be definedbased on rules created by a human user or generated from automatedanalysis of wave data (e.g., clustering analysis).

In the model training 1212 in this implementation, a final set of allwave data marked for inclusion is used to train the prediction model.The model hyperparameters are determined using a non-convex optimizationalgorithm (e.g., the Bayesian optimization algorithm), with the goal ofoptimizing the average model performance in repeated k-foldcross-validation. Multiple approaches are possible: classicalregression; regression with a custom loss function, classificationapproach, etc.

In the output evaluation 1214 in this implementation, output of themodel is evaluated on new SOs which were not used in the training. Thesubject's SO wave features are calculated according to steps 1202-408,and a set of ground-truth annotated SO wave features are created as aground truth for this analysis. If the calculated and manual SO wavefeatures are similar enough, the model passes the output evaluation.

FIG. 13 shows an example process 1300 that can be performed by thesystem 1100. For example, the process 1300 can be performed to process aportion of, or an entire, sleep recording and storing the resulting datafor offline or real-time analysis.

In the process 1300, data of a portion or entire sleep session isreceived 1302. For example, a headband worn by a subject is used togenerate EEG data while the subject is sleeping. N characteristic eventssuch as waveforms are detected 1304 in the data. Fiducial points areidentified 1306 in a waveform, for example to mark a point ofmeasurement within the waveform. Features are extracted and labels areassigned to events 1308 in a waveform, for example finding a differencein timing of various fiducial points. Event data is saved 1310 to recordinformation such as types, fiducial points, features, timestamps,subject identification (ID) data, recording ID data, etc. 1306-1310 canbe repeated for each of the N events.

FIG. 14 shows an example process 1400 for generating stimulus to asubject based on determined timings of electrophysiological events ofthe subject. The process 1400 can be performed, for example, by thephysiological sensors 1104, a data source 1402, the training computerhardware 1110, and a wearable stimulation device 1404 (e.g., dataacquisition device 1001 that includes at least one stimulation devicesuch as sound wave generator 1017), though other components may be usedto perform the process 1400 or other similar processes.

The physiological sensors 1104 sense brain activity measures 1406 andthe training computer hardware 1110 can receive data-streams from thesensors 1104. For example, training subjects can be identified and givena head-worn device with one or more sensors 1104 to wear while theysleep. EEG data, or data used to generate EEG data, is sensed from thetraining subjects and used by the training computer hardware 1110.

One or more data sources 1402 provide 1410 subject data. For example,metadata for the EEG data can be stored and provided by the data source1402. This metadata can include information about the subject (e.g.,demographic data, records of informed consent), tagging data createdafter the EEG data is generated and stored to disk (e.g., well after thetraining subject's sleep session has ended), or other appropriate data.

The training computer hardware 1110 can generate 1412 training data. Forexample, EEG data and subject data can be aggregated to match relevantEEG data with corresponding subject data. Epochs of the EEG data can beidentified and examined to identify waveforms that conform totarget-waveforms, with waveforms lacking sufficiently similar morphologyexcluded from the training data, etc. Features of the morphology (orother properties, e.g., frequency-domain properties) of the waves can begenerated according to a ruleset stored in computer memory and appliedto a waveform.

The training computer hardware 1110 can generate 1414 one or more SOclassifiers with the training data. For example, a machine-learningmodel can be given, as input, a subset of early features of a waveformand given, as target output (e.g., a point of decision value), the later(e.g., occurring after a defined point of decision) features of thewaveform, and the model can be trained to identify relationships betweenthe input and output data.

A wearable stimulation device (and/or related controlling computerhardware) 1404 can receive 1416 the SO classifier. For example, a userof the device 1404 may set up a user profile and provide theirdemographic information, and a demographically-matched orsubject-specific classifier can be accessed and used for that user.

The wearable stimulation device 1404 receives 1418 a data-stream for thesubject. For example, the data-stream can include a real-time EEG signalgenerated by one or more EEG sensors of the device 1404. As the subjectwears the device 1404 and sleeps, the EEG sensors can gather data ofongoing brain activity of the subject within a single sleep session.

The wearable stimulation device 1404 identifies 1420 real-time datarecording a partial SO (e.g., the start of the SO up to the point ofdecision as defined by a particular classifier). For example, the usermay be sleeping and experiencing a SO. At the beginning of the SO, thedevice 1404 can identify the start of the SO based on the EEG data andmark that as time=0 for that SO. Before the end of that SO, the EEG willtherefore record an incomplete SO of the ongoing brain activity.

Waveform detection (e.g. detection of recordings of a partial SO fromreal-time data) can be conducted using specific criteria on amplitudesand durations which can be determined from the fiducial points of thedetected waveform (see, e.g., FIG. 11C). These criteria form rules thatdefine a waveform to be detected in real-time, and are adjusted to theproperties of the real-time signal, either manually or by automatedprocesses. In many cases waveform does not have the same morphology inthe offline-filtered signal (the ground-truth morphology) as it has inthe real-time-filtered signal with phase/amplitude filter distortions.Therefore, real-time waveform detection rules can be optimised toachieve highest real-time detection accuracy of waveforms which areannotated using an offline-filtered signal.

The wearable stimulation device 1404 extracts 1422 SO features for theSO. For example, before the end of the SO, while the subject isexperiencing the SO, the device 1404 can identify one or more timestampsof one or more features (e.g., some, but not all, of the features shownin FIG. 11B). These features may be identified by a number ofmilliseconds after the time=0 point discussed previously, or by anothertechnologically-appropriate scheme.

In some cases, the SO features that are extracted are created using oneor more fiducial points selected from the group (SO Group) consisting ofi) a positive-to-negative zero-crossing (zx1), ii) anegative-to-positive zero-crossing (zx2), iii) a point after zx1 atwhich a slope of the data-stream falls under a negative threshold(steady1), iv) SO negative peak timing (neg_time), v) a point before zx2at which the data-stream falls under a defined positive threshold(steady2), vi) a SO positive peak timing (pos_time), and vii) a pointsat which data-stream value exceeds a defined percentage of the SOnegative peak amplitude (neg_percent). In some cases, the features aremeasures of timing or EEG signal value differences of two fiducialpoints. In some cases, a fiducial point may be used as a feature. Aswill be appreciated, the SO features may be fewer than these features,and/or may include other features.

The wearable stimulation device 1404 determines 1424, in real-time, oneor more predicted SO timings. For example, before this SO is completed,the device 1404 can generate a prediction of a time point for anas-of-yet not experienced or sensed feature or target morphology of thewaveform. In some cases, this predicted SO timing is selected from theSO Group. In some cases, this predicted SO timing is different than theSO Group.

To create the predicted SO timings, the device 1404 can submit, to theSO classifier, the already-sensed features of the SO before the SO ends,while the SO is ongoing, concurrent with the subject experiencing theSO. As described in this document, the SO classifier can be created byuse of training on a dataset of training-SO and matching training-SOtimings (e.g., 1406-612).

The wearable stimulation device 1404 engages 1426 a stimulation based onthe predicted SO timing. For example, the device 1404 can engage thestimulation to provide the subject with a stimulation signal at thepredicted SO timing so that the signal is received by the subject whilethe brain activity of the subject is still generating the same SO as hasbeen discussed in 1420. As will be appreciated, the process 1426 caninclude determinations, for a given SO, to or not to engage stimulation.That is to say, the process 1426 can determine i) to stimulate and ii)when to stimulate, or can determine i) not to stimulate in which caseii) no stimulation timing need be determined for that SO. Thistechnology may be configured to account for previous stimulationdeterminations when determining an upcoming stimulation timing. Forexample, the threshold to determine not to stimulate may begin at alower value (e.g., 0.6) and increase for each sequential determinationto stimulate (e.g., by 0.05) to a maximum value (e.g., 0.8). By use ofsuch a scheme, the threshold to skip a stimulation is lower when asequence of recent stimulations were provided, but higher to skip astimulation if no stimulation has been provided recently.

In some cases, engaging the stimulation signal involves calculating adelay interval to account for, for example, hardware delay, estimatedreal-time filtering delay, etc. This can include determining a delayinterval based on the predicted SO timing within a waveform (e.g., attime=973) minus the current time in the waveform (e.g., at time 933, fora difference of 120). Then, after delaying for the time interval (e.g.,40 milliseconds) from the completion of the calculation, sending anactivation command to the stimulation device.

In some cases, engaging the stimulation signal involves determining if awaveform is an atypical waveform and refraining from engaging thestimulation for that waveform. In some cases, engaging the stimulationsignal involves determining that a waveform is a typical waveform andengaging the stimulation for the waveform responsive to determining thatthe waveform is a typical waveform.

With the method 1400, real-time stimulation of a sleeping subject can besupplied relative to an ongoing SO event. This can allow for superiorstimulation timing, providing stimulation to improve the health,wellness, or other function of the subject. Because the prediction canbe performed in much less time than the length of time that a given SOtakes, the beginning portion of a SO can be used to predict timing oflater portions of the SO when stimulation is to be provided. As will beappreciated, this is an advantage compared to other systems in whichhistorical SO or EEG data is used to retrodict (sometimes called apostdiction) timing of events that are already experienced, recorded,saved to disk, and only then analyzed.

As previously described, the use of a brain age metric (or anothermetric that measures brain function can be combined with thesetechniques to delivery stimulation. Some examples of such a combinationare described here.

Neurostimulation can be applied to enhance brain function, which can bemeasured using the brain age metric. For example, a baseline brain agemetric can be collected for a subject before treatment (e.g., atTime=T₀). Then neurological stimulation can be applied using the timingdeterminations described above to rejuvenate brain function(s) activatedin slow-wave sleep such as memory consolidation, processing speed,hormone activation, glymphatic flow (clearing toxic metabolicbyproducts) and HRV (mood, interpersonal relationships). After treatment(e.g., after a single stimulation exposure in one sleep session, after acourse of treatment over many sleep sessions), a post-treatment brainage metric can be collected at a later time (e.g., at Time=T₁). Then,depending on the outcome, the same treatment can be continued, treatmentcan be modified, etc. and subsequent brain age metrics can be collected(e.g., at Time=T₂, T₃, T₄ . . . T_(N)).

In some cases, audio stimulation can be combined with other therapeutics(e.g., drug therapy) to enhance the efficacy. In some cases,Schizophrenia, believed to be associated with spindle deficit, can betreated with drugs and audio stimulation to enhance spindle activity andaccelerate the treatment compared to drug treatment alone.

In some cases, TBI patients can be treated with combination of audiostimulation and drug therapy. It is believed that TBI patients sufferfrom poor synchronization between SOs and spindles. Therefore, audiostimulation can work in conjunction with drug treatment to enhancespindle coupling and accelerate recovery.

In some cases, audio stimulation can be used to alleviate side effectsof a given medication or disease. As will be appreciated, many mentaldisorders and their traditional drug therapies can produce unwanted sideeffects that either reduce the quality of life of the patients, or causethem to halt the drug therapy because they perceive the side effects tobe worse than the condition being treated. Use of audio stimulation,even if not used to treat the mental disorders itself, it could have atremendous impact on the quality of life for individuals with thesedisorders by reducing the side effects discussed above. By reducing theside effect of a drug with audio stimulation, a patient may be able totolerate the drug where they would not be able to otherwise. Some of thesymptoms and side effects that can be reduced include, but are notlimited to, impaired memory, reduced HRV, increased sympathetic nervoussystem, elevated cortisol levels, chronic inflammation, decreased immuneresponse, fatigue, and increased insulin resistance. Audio stimulationprovided with technology described in this document can be beneficialfor each of these.

Brain age metrics can be used for diagnostics and risk assessments. Forexample, by using brain function assessments and pattern recognition ofunique brain wave characteristics (e.g. changes in sleep architecture,sleep spindle deficits, SO-Spindle coupling) clinicians can performearlier diagnosis or assess severity of various conditions. Examples ofthese conditions include, but are not limited to, the following.Pre-symptomatic risk assessment for mild cognitive impairment (MCI) orAlzheimer's Disease may be performed. Audio stimulation can be used byearly stage MCI patients to slow degradation in brainsignalling/communication (i.e. SO-spindle coupling). For TBI patients,loss of synchronization (i.e. poorer SO-spindle coupling) is common.Therefore, a brain age metric can be used as a diagnostic to measureseverity of TBI. For example, severe changes in brain age and brain ageexplanations (e.g., SHapley Additive exPlanations or SHAP) in post vs.pre injury. Audio stimulation of SWS can enhance neuronal communicationand restore synchronization between the hippocampus and prefrontalcortex.

For Long COVID patients, objective measure of brain function can be usedto assess presence or severity of Long COVID's brain fog. Furthermore,recovery of these symptoms can be accelerated with audio stimulation.

Early pre-symptom detection, risk detection, and diagnosis can beperformed for many diseases, e.g., mild cognitive impairment (MCI),pre-symptomatic Alzheimer's disease (AD), or Schizophrenia. For example,prodromal Schizophrenia can be identified with this technology based onobjective measures, even before the subject is aware of the symptoms orbefore the symptoms have any noticeable impact on their quality of life.Schizophrenia (and other diseases) have unique brain wavecharacteristics (e.g. sleep spindle deficits) which can be identifiedwith this technology. By performing objective diagnostics, Schizophreniacan be detected early in subjects where subjective diagnostic criteriais likely to miss the symptoms for diagnosis. For example, a patientwith excellent executive function and a robust support structure maymask or camouflage symptoms—even from themselves—in the early stages ofthe disease. By earlier diagnosis with this technology treatment can bedelivered to slow or halt the progression before it does impact thesubject.

This technology can be used to measure the impact of an intervention orcombination of interventions. For example, this technology is able toprovide an assessment that is specific enough to measure the impact ofvarious treatments such as lifestyle changes, behavioral choices,medical management (managing hypertension, diabetes, etc.) on brain age.

Bi-directional relationships between sleep and medical conditions can bemeasured. Said another way, this technology can be used to create avirtuous cycle of reducing a symptom that impairs sleep, allowing formore sleep causing for better outcomes of the disease that was impairingsleep in the first place. For example, enhancing SWS can help addressthe negative symptoms of diseases (cognitive deficits associated withschizophrenia and depression, improved HRV), and improved disease statescan enhance the brain age metric for a subject. Enhanced SWS can thenresult in the reduction of medical comorbidities of mental illness,inflammation, stress, etc., that lead to premature death andneurodegeneration.

Example embodiments include:

-   -   1. A method for providing stimulation to a subject, the method        comprising:        -   receiving a data-stream for the subject, the data-stream            comprising a real-time EEG signal generated by one or more            EEG sensors gathering data of ongoing brain activity of the            subject;        -   identifying in the data-stream a record of a current slow            oscillation (SO) that contains data of an incomplete SO of            the ongoing brain activity;        -   extracting one or more SO features for the current SO from            the record of the current SO;        -   determining, from the SO features, one or more predicted SO            values, the predicted SO values each being a prediction of a            future event at which the current SO will exhibit a target            morphology.    -   2. The method of embodiment 1, wherein the method further        comprises:        -   engaging a stimulation device to provide the subject with a            stimulation signal based on the predicted SO values such            that the subject receives the stimulation signal while the            brain activity of the subject is generating the current SO.    -   3. The method of embodiment 2, wherein engaging the stimulation        device comprises:        -   determining a delay interval based on the predicted SO            values;        -   delaying for the delay interval; and        -   sending an activation command to the stimulation device upon            expiration of the delay interval.    -   4. The method of embodiment 2, wherein engaging the stimulation        device comprises determining that the current SO is a typical        SO.    -   5. The method of embodiment 1, wherein the one or more SO        features that are extracted are selected from the group (SO        Group) consisting of i) a positive-to-negative zero-crossing        (zx1), ii) a negative-to-positive zero-crossing (zx2), iii) a        point after zx1 at which a slope of the data-stream falls under        a negative threshold (steady1), iv) SO negative peak timing        (neg_time), v) a point before zx2 at which the data-stream falls        under a defined positive threshold (steady), vi) a SO positive        peak timing (pos_time), and vii) a points at which data-stream        value exceeds a defined percentage of the SO negative peak        amplitude (neg_percent).    -   6. The method of embodiment 5, wherein the one or more predicted        SO values are also selected from the SO Group.    -   7 The method of embodiment 5, wherein the one or more SO values        are different than the SO Group.    -   8. The method of embodiment 2, wherein determining, from the SO        features, one or more predicted SO values comprises submitting,        to a SO-classifier, the SO features and receiving the predicted        SO values.    -   9. The method of embodiment 8, wherein the SO-classifier        produces a point of decision.    -   10. The method of embodiment 8, wherein the SO-classifier is        created via training on a dataset of training-SO features and        matching training-SO values.    -   11. The method of embodiment 8, wherein the dataset is        constructed to exclude atypical training-SO features.    -   12. The method of embodiment 8, wherein the classifier is        retrained using the SO features of a single night's sleep during        the single night's sleep.    -   13. The method of embodiment 8, wherein the classifier is        trained for a specific morphological type of SO.    -   14. The method of embodiment 8, wherein the classifier is        trained for the subject using training data from the subject.    -   15. The method of embodiment 8, wherein the classifier is        trained in real-time using the data from a current sleep        session.    -   16. The method of embodiment 1, wherein determining of one or        more predicted SO values is responsive to determining that the        subject is in a particular sleep stage    -   17. A system for providing stimulation to a subject, the system        comprising:        -   a data acquisition device comprising a body, one or more EEG            sensors, and at least one stimuli generator;        -   one or more processors; and        -   memory storing instructions that, when executed by the            processors, cause the processors to perform operations            comprising:            -   receiving a data-stream for the subject, the data-stream                comprising a real-time EEG signal generated by the one                or more EEG sensors gathering data of ongoing brain                activity of the subject;            -   identifying in the data-stream a record of a current                slow oscillation (SO) that contains data of an                incomplete SO of the ongoing brain activity;            -   extracting one or more SO features for the current SO                from the record of the current SO;            -   determining, from the SO features, one or more predicted                SO, the predicted SO timings each being a prediction of                a future time at which the current SO will exhibit a                target morphology.    -   18. The system of embodiment 16, wherein the body is a headband        that includes a curved shape that is configured to extend around        each ear of a subject and under a nape of the back of a        subject's head.    -   19. The system of embodiment 16, wherein the operations further        comprise:        -   engaging the at least one stimuli generator to provide the            subject with a stimulation signal at the predicted SO values            such that the subject receives the stimulation signal while            the brain activity of the subject is generating the current            SO.    -   20. The system of embodiment 16, wherein the stimuli generator        generates audio stimuli.    -   21. The system of embodiment 16, wherein determining, from the        SO features, one or more predicted SO values comprises        submitting, to a SO-classifier, the SO features and receiving        the predicted SO values.    -   22. The system of embodiment 16, wherein the one or more SO        features that are extracted are selected from the group (SO        Group) consisting of i) a positive-to-negative zero-crossing        (zx1), ii) a negative-to-positive zero-crossing (zx2), iii) a        point after zx1 at which a slope of the data-stream falls under        a negative threshold (steady1), iv) SO negative peak timing        (neg_time), v) a point before zx2 at which the data-stream falls        under a defined positive threshold (steady), vi) a SO positive        peak timing (pos_time), and vii) a points at which data-stream        value exceeds a defined percentage of the SO negative peak        amplitude (neg_percent).

Although a few implementations have been described in detail above,other modifications are possible. Moreover, other mechanisms forperforming the systems and methods described in this document may beused. In addition, the logic flows depicted in the figures do notrequire the particular order shown, or sequential order, to achievedesirable results. Other steps may be provided, or steps may beeliminated, from the described flows, and other components may be addedto, or removed from, the described systems. Accordingly, otherimplementations are within the scope of the following claims.

What is claimed is:
 1. A system for use in determining metrics of asubject, the system comprising: one or more processors; and memorystoring instructions that, when executed by the processors, cause theprocessors to perform operations comprising: receiving physiologicalmeasures of the subject recorded at least partly while the subject isasleep; receiving demographic data for the subject, the demographic datacomprising a chronological age for the subject when the physiologicalmeasures were recorded; generating, using the physiological measures andfrom the demographic data, segmented training-data that specifies aplurality of epochs of time, and data for the subject in each epoch;generating, using the segmented training-data, sleep-structure featuresfor the subject; selecting a subset of the sleep-structure features asselected features; generating one or more function-metric classifiersusing the selected features, comprising training a model that defines atleast one relationship between the physiological measures and thechronological age, the function-metric classifier configured to:receive, as input, new physiological measures; and provide, as output, afunction-metric value determined based on the defined relationshipbetween the physiological measures and the chronological age.
 2. Thesystem of claim 1, wherein the demographic data further comprises atleast one of the group consisting of an identifier, a gender,sociodemographic data, medical data, behavioral data, and lifestyle datathat have been entered by the subject into an input device of thesystem.
 3. The system of claim 1, wherein the physiological measures ofthe subject comprise at least one of the group consisting of a frontalelectroencephalography (EEG) channel, two EEG channels, foreheadphotoplethysmography (PPG), blood oxygen saturation (SpO2),electromyography (EMG), electrooculography (EOG), electrodermal activity(EDA), and actigraphy data.
 4. The system of claim 1, wherein: thephysiological measures of the subject were recorded at least partlywhile the subject is asleep and provided with at least one stimuli ofthe group consisting of audio stimuli, light stimuli, vibratory,electrical stimuli, open-loop stimuli, and closed-loop stimuli; and thephysiological measures comprising timing info defining timing of stimuliprovided to the subject.
 5. The system of claim 1, wherein generating,using the physiological measures and from the demographic data,segmented training-data comprises: applying band-pass filters to atleast some of the physiological measures; combining at least two EEGsignals to create a virtual EEG signal; extracting heartbeat times fromPPG signals by detecting peaks in the PPG signals; segmenting thephysiological measures into a plurality of epochs of time; generating,for each epoch of time, at least one of the group consisting of multipletime-domain features, frequency domain features, and nonlinear orcomplex signal descriptives; and accessing tagging data that tags eachepoch of time with a sleep stage from the group consisting ofwakefulness, rapid eye movement (REM) sleep, non-REM sleep stage 1 (N1),non-REM sleep stage 2 (N2), non-REM sleep stage 3 (N3), and non-REMsleep stage 4 (N4).
 6. The system of claim 1, wherein generating, usingthe segmented training-data, sleep-structure features for the subjectcomprises: determining macrostructure features for the subjectdescribing at least one of the group consisting of sleep-stage duration,sleep-stage percentage, sleep-stage transition probability, sleepfragmentation, and awakenings; determining microstructure features forthe subject describing at least one of the group consisting ofstage-specific EEG features, waveform-specific EEG features, andstimulus-response EEG features; determining cardiac features for thesubject using at least one of the group consisting of heartbeat timesand tagging data that tags epochs of time with sleep stage; anddetermining respiratory features for the subject describing at least oneof the group consisting of blood oxygenation, heart rate, heart beattimes, and sleep apnea, using at least one of the group consisting ofblood oxygen saturation (SpO2) and the tagging data.
 7. The system ofclaim 1, wherein selecting a subset of the sleep-structure features asselected features comprises: transforming at least one of a plurality offeatures selected from the group consisting of macrostructure features,microstructure features, cardiac features, and respiratory features; andaggregating sleep-structure features from multiple sleep-sessions. 8.The system of claim 1, wherein selecting a subset of the sleep-structurefeatures as selected features comprises: identifying the subset of thesleep-structure features as those features most predictive of thechronological age of the demographic data.
 9. The system of claim 1,wherein selecting a subset of the sleep-structure features as selectedfeatures comprises sequentially calculating the cross-validated meanabsolute error (MAE) with a regression model, wherein the regressionmodel is an extreme learning machine (ELM) regressor.
 10. The system ofclaim 1, wherein training the model that defines at least onerelationship between the physiological measures and the chronologicalage comprises determining hyperparmeters for the model using a Bayesianoptimization algorithm targeting a repeated k-fold cross-validationusing at least one of the group consisting of a regression; a regressionwith a loss function based on a residual-label covariance analysis, anda deep label distribution algorithm.
 11. The system of claim 1, whereintraining a model that defines at least one relationship between thephysiological measures and the chronological age comprises refining themodel to reduce age-dependent bias.
 12. The system of claim 1, whereinthe classifier is further configured to provide, as output, at least oneof the group consisting of a confidence value, avariance-from-chronological-age value, a model interpretation, aninterpretation of the model's output, a human-readable instructiondisplayable to a user of an output device, and an automation-instructionthat, when executed by an automated device causes the automated deviceto actuate.
 13. The system of claim 1, wherein the operations furthercomprise distributing the function-metric classifiers to a plurality ofuser devices that are configured to sense new physiological measures ofother subjects at least partly while the other subjects are asleep. 14.The system of claim 1, wherein the operations further comprise:receiving new physiological measures of the subject recorded at leastpartly while the subject is asleep and recorded after thefunction-metric classifiers have already been generated; submitting thenew physiological measures to at least one of the function-metricclassifiers as the input; and receiving as output from the at least onefunction-metric classifier the function-metric value.
 15. The system ofclaim 1, wherein the operations further comprise estimating a predictedfunction-metric for the subject to represent a measure of predictedfuture physiological measures at a future chronological age.
 16. Asystem for use in determining metrics of a subject, the systemcomprising: one or more processors; and memory storing instructionsthat, when executed by the processors, cause the processors to performoperations comprising: receiving physiological measures of the subjectrecorded at least partly while the subject is asleep; receivingdemographic data for the subject, the demographic data comprising achronological age for the subject when the physiological measures wererecorded; generating one or more function-metric classifiers usingselected features, comprising training a model that defines at least onerelationship between physiological measures and a chronological age, thefunction-metric classifier configured to: receive, as input, newphysiological measures; and provide, as output, a function-metric valuedetermined based on the defined relationship between the physiologicalmeasures and the chronological age; wherein the selected features arecreated by: generating, using the physiological measures and from thedemographic data, segmented training-data that specifies a plurality ofepochs of time, and data for the subject in each epoch; generating,using the segmented training-data, sleep-structure features for thesubject; and selecting a subset of the sleep-structure features theselected features.
 17. The system of claim 16, the physiologicalmeasures of the subject were recorded at least partly while the subjectis asleep and provided with at least one stimuli of the group consistingof audio stimuli, light stimuli, vibratory, electrical stimuli,open-loop stimuli, and closed-loop stimuli; and the physiologicalmeasures comprising timing info defining timing of stimuli provided tothe subject.
 18. The system of claim 16, wherein generating, using thephysiological measures and from the demographic data, segmentedtraining-data comprises: applying band-pass filters to at least some ofthe physiological measures; combining at least two EEG signals to createa virtual EEG signal; extracting heartbeat times from PPG signals bydetecting peaks in the PPG signals; segmenting the physiologicalmeasures into a plurality of epochs of time; generating, for each epochof time, at least one of the group consisting of multiple time-domainfeatures, frequency domain features, and nonlinear or complex signaldescriptives; and accessing tagging data that tags each epoch of timewith a sleep stage from the group consisting of wakefulness, rapid eyemovement (REM) sleep, non-REM sleep stage 1 (N1), non-REM sleep stage 2(N2), non-REM sleep stage 3 (N3), and non-REM sleep stage 4 (N4). 19.The system of claim 16, wherein generating, using the segmentedtraining-data, sleep-structure features for the subject comprises:determining macrostructure features for the subject describing at leastone of the group consisting of sleep-stage duration, sleep-stagepercentage, sleep-stage transition probability, sleep fragmentation, andawakenings; determining microstructure features for the subjectdescribing at least one of the group consisting of stage-specific EEGfeatures, waveform-specific EEG features, and stimulus-response EEGfeatures; determining cardiac features for the subject using at leastone of the group consisting of heartbeat times and tagging data thattags epochs of time with sleep stage; and determining respiratoryfeatures for the subject describing at least one of the group consistingof blood oxygenation, heart rate, heart beat times, and sleep apnea,using at least one of the group consisting of blood oxygen saturation(SpO2) and the tagging data.
 20. The system of claim 16, whereinselecting a subset of the sleep-structure features as selected featurescomprises: transforming at least one of a plurality of features selectedfrom the group consisting of macrostructure features, microstructurefeatures, cardiac features, and respiratory features; and aggregatingsleep-structure features from multiple sleep-sessions.